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Purpose: The Functional Assessment of Cancer Therapy item (FACT-GP5) has the potential to provide an understanding of global treatment tolerability from the patient perspective. Longitudinal evaluations of the FACT-GP5 and challenges posed by data missing-not-at-random (MNAR) have not been explored. Robustness of the FACT-GP5 to missing data assumptions and the responsiveness of the FACT-GP5 to key side-effects are evaluated. Methods: In a randomized, double-blind study (NCT00065325), postmenopausal women (n = 618) with hormone receptor-positive (HR+), advanced breast cancer received either fulvestrant or exemestane and completed FACT measures monthly for seven months. Cumulative link mixed models (CLMM) were fit to evaluate: (1) the trajectory of the FACT-GP5 and (2) the responsiveness of the FACT-GP5 to CTCAE grade, Eastern Cooperative Oncology Group (ECOG) Performance Status scale, and key side-effects from the FACT. Sensitivity analyses of the missing-at-random (MAR) assumption were conducted. Results: Odds of reporting worse side-effect bother increased over time. There were positive within-person relationships between level of side-effect bother (FACT-GP5) and severity of other FACT items, as well as ECOG performance status and Common Terminology Criteria for Adverse Events (CTCAE) grade. The number of missing FACT-GP5 assessments impacted the trajectory of the FACT-GP5 but did not impact the relationships between the FACT-GP5 and other items (except for nausea [FACT-GP2]). Conclusions: Results support the responsiveness of the FACT-GP5. Generally speaking, the responsiveness of the FACT-GP5 is robust to missing assessments. Missingness should be considered, however, when evaluating change over time of the FACT-GP5. Trial Registration: NCT00065325. Trial Registration Year: 2003. [ABSTRACT FROM AUTHOR]

Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random. [ABSTRACT FROM AUTHOR]

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures. [ABSTRACT FROM AUTHOR]

In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife estimator of the standard error was proposed as a way to obtain deterministic treatment effect estimates and correct frequentist inference. For both multiple and CMI, a mixed model for repeated measures (MMRM) is often used for the imputation model, but this can be computationally intensive to fit to multiple data sets (e.g. the jackknife samples) and lead to convergence issues with complex MMRM models with many parameters. Therefore, a step-wise approach based on sequential linear regression (SLR) of the outcomes at each visit was developed for the imputation model in the multiple imputation framework, but similar developments in the CMI framework are lacking. In this article, we fill this gap in the literature by proposing a SLR approach to implement RBI in the CMI framework, and justify its validity using theoretical results and simulations. We also illustrate our proposal on a real data application. [ABSTRACT FROM AUTHOR]

Purpose: Non-response (NR) to patient-reported outcome (PRO) questionnaires may cause bias if not handled appropriately. Collecting reasons for NR is recommended, but how reasons for NR are related to missing data mechanisms remains unexplored. We aimed to explore this relationship for intermittent NRs. Methods: Patients with multiple myeloma completed validated PRO questionnaires at enrolment and 12 follow-up time-points. NR was defined as non-completion of a follow-up assessment within seven days, which triggered contact with the patient, recording the reason for missingness and an invitation to complete the questionnaire (denoted "salvage response"). Mean differences between salvage and previous on-time scores were estimated for groups defined by reasons for NR using linear regression with clustered standard errors. Statistically significant mean differences larger than minimal important difference thresholds were interpreted as "missing not at random" (MNAR) mechanism (i.e. assumed to be related to declining health), and the remainder interpreted as aligned with "missing completely at random" (MCAR) mechanism (i.e. assumed unrelated to changes in health). Results: Most (7228/7534 (96%)) follow-up questionnaires were completed; 11% (802/7534) were salvage responses. Mean salvage scores were compared to previous on-time scores by reason: those due to hospital admission, mental or physical reasons were worse in 10/22 PRO domains; those due to technical difficulties/procedural errors were no different in 21/22 PRO domains; and those due to overlooked/forgotten or other/unspecified reasons were no different in any domains. Conclusion: Intermittent NRs due to hospital admission, mental or physical reasons were aligned with MNAR mechanism for nearly half of PRO domains, while intermittent NRs due to technical difficulties/procedural errors or other/unspecified reasons generally were aligned with MCAR mechanism. [ABSTRACT FROM AUTHOR]

We consider model averaging estimation problem in the linear regression model with missing response data, that allows for model misspecification. Based on the 'complete' data set for the response variable after inverse propensity score weighted imputation, we construct a leave-one-out cross-validation criterion for allocating model weights, where the propensity score model is estimated by the covariate balancing propensity score method. We derive some theoretical results to justify the proposed strategy. Firstly, when all candidate outcome regression models are misspecified, our procedures are proved to achieve optimality in terms of asymptotically minimizing the squared loss. Secondly, when the true outcome regression model is among the set of candidate models, the resulting model averaging estimators of the regression parameters are shown to be root-n consistent. Simulation studies provide evidence of the superiority of our methods over other existing model averaging methods, even when the propensity score model is misspecified. As an illustration, the approach is further applied to study the CD4 data. [ABSTRACT FROM AUTHOR]

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations. [ABSTRACT FROM AUTHOR]

Distributed machine learning has received much attention for more than two decades. Yet, it is still a challenge to achieve acceptable performance in practical scenarios when some features of data samples are missing. Although some imputation methods have been proposed for handling missing data, their performance deteriorates significantly when data are heterogeneously distributed over different nodes in the network. Considering this, in this article, we first propose a general Gaussian mixture model (GMM) consisting of both public and personalized components for modeling the homogeneous and the heterogeneous parts of data distribution, respectively. Then, we develop a distributed personalized expectation–maximization method based on knowledge transfer (KT-dpEM) to estimate the parameters of the proposed general GMM. After that, based on the estimated general GMM, missing data are imputed using the posterior conditional mean. Experimental results show that the proposed KT-dpEM algorithm has better imputation accuracy, higher robustness against different missing probabilities and better classification performance in the downstream classification tasks, compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study. [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Mediation, moderation, and moderated mediation are common in behavioral research models. Several tools are available for estimating indirect effects, conditional effects, and conditional indirect effects and forming their confidence intervals. However, there are no simple-to-use tools that can appropriately form the bootstrapping confidence interval for standardized conditional indirect effects. Moreover, some tools are restricted to a limited type of models. We developed an R package, manymome, which can be used to estimate and form confidence intervals for indirect effects, conditional effects, and conditional indirect effects, standardized or not, using a two-step approach: model parameters are estimated either by structural equation modeling using lavaan or by a set of linear regression models using lm, and then the coefficients are used to compute the requested effects and form confidence intervals. It can be used when there are missing data if the model is fitted by structural equation modeling. There are only a few limitations on some aspects of a model, and no inherent limitations on the number of predictors, the number of independent variables, or the number of moderators and mediators. The goal is to have a tool that allows researchers to focus on model fitting first and worry about estimating the effects later. The use of the model is illustrated using a few numerical examples, and the limitations of the package are discussed. [ABSTRACT FROM AUTHOR]

In this paper we deal with parametric estimation of the copula in the case of missing data. The data items with the same pattern of complete and missing data are combined into a subset. This approach corresponds to the MCAR model for missing data. We construct a specific Cramér–von Mises statistic as a sum of such statistics for the several missing data patterns. The minimization of the statistic gives the estimators for the parameters. We prove asymptotic normality of the parameter estimators and of the Cramér–von Mises statistic. [ABSTRACT FROM AUTHOR]

While advances continue to be made in model-based clustering, challenges persist in modeling various data types such as panel data. Multivariate panel data present difficulties for clustering algorithms because they are often plagued by missing data and dropouts, presenting issues for estimation algorithms. This research presents a family of hidden Markov models that compensate for the issues that arise in panel data. A modified expectation–maximization algorithm capable of handling missing not at random data and dropout is presented and used to perform model estimation. [ABSTRACT FROM AUTHOR]

Missing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included Random Forest (RF), PCA/MCA, factorial analysis for mixed data (FAMD), hotdeck, predictive mean matching (PMM), random samples from observed values (RSOV), and a multi-method (MM) approach for the three missingness mechanisms (MCAR, MAR, and MNAR) at levels of 5%, 10%, 20%, 30%, and 40% missingness. This study also compared single imputation with an adapted multiple imputation method derived from the R package “mice”. The results showed that the adapted multiple imputation technique always outperformed single imputation for the same method. The best performing methods were most often RF and MM, and other commonly successful methods were PCA/MCA and PMM multiple imputation. Across all criteria, the amount of missingness was the most important parameter for imputation accuracy. While this study found that some imputation methods performed better than others for the control dataset, each imputation method has advantages and disadvantages. Imputation remains a promising solution for datasets containing missingness; however when making a decision it is essential to consider dataset structure and research goals. [ABSTRACT FROM AUTHOR]

Body Mass Index (BMI) trajectories are important for understanding how BMI develops over time. Missing data is often stated as a limitation in studies that analyse BMI over time and there is limited research exploring how missing data influences BMI trajectories. This study explores the influence missing data has in estimating BMI trajectories and the impact on subsequent analysis. This study uses data from the English Longitudinal Study of Ageing. Distinct BMI trajectories are estimated for adults aged 50 years and over. Next, multiple methods accounting for missing data are implemented and compared. Estimated trajectories are then used to predict the risk of developing type 2 diabetes mellitus (T2DM). Four distinct trajectories are identified using each of the missing data methods: stable overweight, elevated BMI, increasing BMI, and decreasing BMI. However, the likelihoods of individuals following the different trajectories differ between the different methods. The influence of BMI trajectory on T2DM is reduced after accounting for missing data. More work is needed to understand which methods for missing data are most reliable. When estimating BMI trajectories, missing data should be considered. The extent to which accounting for missing data influences cost-effectiveness analyses should be investigated. [ABSTRACT FROM AUTHOR]

Environmental health research aims to assess the impact of environmental exposures, making it crucial to understand their effects due to their broad impacts on the general population. However, a common issue with measuring exposures using bio-samples in laboratory is that values below the limit of detection (LOD) are either left unreported or inaccurately read by machines, which subsequently influences the analysis and assessment of exposure effects on health outcomes. We address the challenge of handling exposure variables subject to LOD when they are treated as either covariates or an outcome. We evaluate the performance of commonly-used methods including complete-case analysis and fill-in method, and advanced techniques such as multiple imputation, missing-indicator model, two-part model, Tobit model, and several others. We compare these methods through simulations and a dataset from NHANES 2013–2014. Our numerical studies show that the missing-indicator model generally yields reasonable estimates when considering exposure variables as covariates under various settings, while other methods tend to be sensitive to the LOD-missing proportions and/or distributional skewness of exposures. When modeling an exposure variable as the outcome, Tobit model performs well under Gaussian distribution and quantile regression generally provides robust estimates across various shapes of the outcome's distribution. In the presence of missing data due to LOD, different statistical models should be considered for being aligned with scientific questions, model assumptions, requirements of data distributions, as well as their interpretations. Sensitivity analysis to handle LOD-missing exposures can improve the robustness of model conclusions. [ABSTRACT FROM AUTHOR]

Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impact active learning's effectiveness in real-world contexts. We conceptualise this degradation by considering the type of non-response present in the data, demonstrating that biased non-response is particularly detrimental to model performance. We argue that biased non-response is likely in contexts where the labelling process, by nature, relies on user interactions. To mitigate the impact of biased non-response, we propose a cost-based correction to the sampling strategy–the Upper Confidence Bound of the Expected Utility (UCB-EU)–that can, plausibly, be applied to any active learning algorithm. Through experiments, we demonstrate that our method successfully reduces the harm from labelling non-response in many settings. However, we also characterise settings where the non-response bias in the annotations remains detrimental under UCB-EU for specific sampling methods and data generating processes. Finally, we evaluate our method on a real-world dataset from an e-commerce platform. We show that UCB-EU yields substantial performance improvements to conversion models that are trained on clicked impressions. Most generally, this research serves to both better conceptualise the interplay between types of non-response and model improvements via active learning, and to provide a practical, easy-to-implement correction that mitigates model degradation. [ABSTRACT FROM AUTHOR]

Archaeologists all over the world face problems regarding complexity and disorganization. Whether surveying, excavating, or doing laboratory analysis, the nature of the evidence of prehistoric societies is fragmented and incomplete. On a global and very general basis, the older the site, the greater the fragmentation, the more the missing data, and the greater the disorganization that the archaeologist must navigate to understand the past. Of course, there are notable exceptions. Most archaeologists consider the topic from the specificity of a particular time, a particular place, and a particular society. In this paper, it is considered in its most non-particular and general format. In order to do so, the paper creates an artificial archaeological region that is surveyed and excavated to a greater and lesser extent and analyzed with a variety of statistical and graphic evaluations. It concludes that when all other things are equal, increasing fragmentation causes far more disorganization and increases complexity than does missing data. Thus, fragmentation is a far more important problem for archaeological interpretation than relatively small amounts of missing data. [ABSTRACT FROM AUTHOR]

Dam health monitoring is an important method for quantitative evaluation of dam safety. After long-term operation, there have missing data in dam monitoring data series inevitably due to the sensor damage or monitoring system failure problem which seriously affects the correctness of dam safety evaluation. The imputation accuracy of missing value is affected by data decomposition, reconstruction, and prediction methods. Therefore, in view of the high-precision imputation model of missing data in dam health monitoring, this paper proposes a data-driven fusion imputation model based on novel mode decomposition and deep learning method. First, the fusion imputation model is constructed based on extreme-point symmetric mode decomposition (ESMD), permutation entropy (PE), and bidirectional gate recurrent unit neural network (BiGRU). The ESMD-PE data preprocessing module can decompose the original data into a series of stable subsequences which can be input into the advanced deep learning BiGRU model to improve the interpolation accuracy. Then, the types of dam missing data and interpolation steps are studied. The engineering example illustrates that the root mean square error of the proposed model is decreased by 55.32% on average compared with four classical imputation models. The ESMD-PE–BiGRU fusion model can effectively simulate the inherent law of dam monitoring data and predict the missing data, which provides complete monitoring data for dam safety analysis. [ABSTRACT FROM AUTHOR]

Environmental signals, acquired, e.g., by remote sensing, often present large gaps of missing observations in space and time. In this work, we present an innovative approach to identify the main variability patterns, in space–time data, when data may be affected by complex missing data structures. We formalize the problem in the framework of functional data analysis, proposing an innovative method of functional principal component analysis (fPCA) for incomplete space–time data. The functional nature of the proposed method permits to borrow information from measurements observed at nearby spatio-temporal locations. The resulting functional principal components are smooth fields over the considered spatio-temporal domain, and can lead to interesting insights in the spatio-temporal dynamic of the phenomenon under study. Moreover, they can be used to provide a reconstruction of the missing entries, also under severe missing data patterns. The proposed model combines a weighted rank-one approximation of the data matrix with a roughness penalty. We show that the estimation problem can be solved using a majorize–minimization approach, and provide a numerically efficient algorithm for its solution. Thanks to a discretization based on finite elements in space and B-splines in time, the proposed method can handle multidimensional spatial domains with complex shapes, such as water bodies with complicated shorelines, or curved spatial regions with complex orography. As shown by simulation studies, the proposed space–time fPCA is superior to alternative techniques for Principal Component Analysis with missing data. We further highlight the potentiality of the proposed method for environmental problems, by applying space–time fPCA to the study of the lake water surface temperature (LWST) of Lake Victoria, in Central Africa, starting from satellite measurements with large gaps. LWST is considered one of the fundamental indicators of how climate change is affecting the environment, and is recognized as an essential climate variable. [ABSTRACT FROM AUTHOR]

In a realistic scenario, the dataset has missing values encountered during the data collection. To effectively build the prediction model, the missingness of the attributes that impact crop growth needs to be appropriately handled in the crop dataset. The study aims to impute missing data in the Wheat crop yield Dataset, consisting of climatic parameters and historical data of 370 districts of Major Wheat Producer states of India. This study plays a vital role in crop estimation or forecasting of production at regular intervals. The imputation techniques that replace missing data have been categorized into Statistical and Machine Learning based Methods. We explored the performance of popular Techniques such as Arithmetic Average Replacement, Median Imputation, Linear Interpolation, Average Imputation by Nearby Districts, K-Nearest Neighbour, Miss Forest, Regression, and MICE. We have also evaluated these methods on the UCI machine learning repository's Bias and Steel energy consumption datasets. These imputed results were fed to the multiple regression prediction models to evaluate the efficiency of the imputation approaches qualitatively. The results conclude that the Arithmetic Average Replacement method provides good results among the statistical methods (R2 = 0.83; RMSE = 0.47; MAE = 0.372; MSE = 0.229), whereas in Machine Learning based methods, Miss Forest Random Forest-based method, and MICE performed well (R2 = 0.80; MAE = 0.3825; MSE = 0.249; RMSE = 0.499) to impute the missing data. We hope our results help the researchers to select the appropriate pre-processing strategies and improve the data quality. [ABSTRACT FROM AUTHOR]

The performance of the statistical classifiers being employed determines how precisely an electronic prosthesis moves its target. The performance of the classifier is correlated with the quality of the acquired data, and it degrades in the presence of incomplete data. Missing data must therefore be recovered in order to enhance classifier performance and subsequently regulate prosthesis performance. However, recovering lost data can be difficult. For the purpose of recovering missing data in electroencephalography (EEG) signals, a fast and accurate Xavier-initialized weighted canonical polyadic (X-WCP) based approach has been described in this study. In comparison to cutting-edge techniques, the Xavier-initialized weights converge quickly since they are close to the optimal solution. This research also shows the effectiveness of the proposed approach for enhancing classifier performance. For the method to be validated, three models are proposed. Classifiers are employed to complete EEG data in the first model, and the results are used as a benchmark. In the second and third models, classifiers are employed on partial and recovered signals. The results demonstrate that when recovered data is provided to classifiers instead of partially complete EEG data, where classification accuracy was 55%, classification accuracy is greatly enhanced (up to 70%). Classification accuracy at the benchmark level was 71%. [ABSTRACT FROM AUTHOR]

Purpose: Many studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured longitudinally, at baseline and at predefined timepoints thereafter. The question is whether, at a given timepoint, patients who return their questionnaire (available cases, AC) have a different QoL than those who do not return their questionnaire (non-AC). Methods: We employed augmented inverse probability weighting (AIPW) to estimate the average QoL of non-AC in two studies on advanced-stage cancer patients. The AIPW estimator assumed data to be missing at random (MAR) and used machine learning (ML)-based methods to estimate answering probabilities of individuals at given timepoints as well as their reported QoL, as a function of auxiliary variables. These auxiliary variables were selected by medical oncologists based on domain expertise. We aggregated results both by timepoint and by time until death and compared AIPW estimates to the AC averages. Additionally, we used a pattern mixture model (PMM) to check sensitivity of our AIPW estimates against violation of the MAR assumption. Results: Our study included 1927 patients with advanced pancreatic and 797 patients with advanced breast cancer. The AIPW estimate for average QoL of non-AC was below the average QoL of AC when aggregated by timepoint. The difference vanished when aggregated by time until death. PMM estimates were below AIPW estimates. Conclusions: Our results indicate that non-AC have a lower average QoL than AC. However, estimates for QoL of non-AC are subject to unverifiable assumptions about the missingness mechanism. [ABSTRACT FROM AUTHOR]

This paper describes a correlation-based batch process for addressing high dimensional imputation problems. There are relatively few algorithms designed to efficiently handle imputation of missing data in high dimensional contexts. Fewer still are flexible enough to natively handle mixed-type data, often requiring lengthy pre-processing to get the data into proper shape, and then post-processing to return the data to usable form. Such decisions as well as assumptions made by many methods (e.g., data generating process) limit their performance, flexibility, and usability. Built on a set of complementary algorithms for nonparametric imputation via chained random forests, I introduce a batching process to ease computational costs associated with high dimensional imputation by subsetting data based on ranked cross-feature absolute correlations. The algorithm then imputes each batch separately, and joins imputed subsets in the final step. The process, hdImpute, is fast and accurate. As a result, high dimensional imputation is more accessible, and researchers are not forced to decide between speed or accuracy. Complementary software is available in the form of an R package, and is openly developed on Github under the MIT public license. In the spirit of open science, collaboration and engagement with the actively developing software are encouraged. [ABSTRACT FROM AUTHOR]

In Bayesian statistics, the most widely used criteria of Bayesian model assessment and comparison are Deviance Information Criterion (DIC) and Watanabe–Akaike Information Criterion (WAIC). We use a multilevel mediation model as an illustrative example to compare different types of DIC and WAIC. More specifically, we aim to compare the performance of conditional and marginal DICs and WAICs, and investigate their performance with missing data. We focus on two versions of DIC ( D I C 1 and D I C 2 ) and one version of WAIC. In addition, we explore whether it is necessary to include the nuisance models of incomplete exogenous variables in likelihood. Based on the simulation results, whether D I C 2 is better than D I C 1 and WAIC and whether we should include the nuisance models of exogenous variables in likelihood functions depend on whether we use marginal or conditional likelihoods. Overall, we find that the marginal likelihood based- D I C 2 that excludes the likelihood of covariate models generally had the highest true model selection rates. [ABSTRACT FROM AUTHOR]

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the many-variables problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy through two Monte Carlo simulation studies and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures. [ABSTRACT FROM AUTHOR]

Data censoring occurs when researchers do not know precise values of data points (e.g., age is 55+ or concentration ≤.001). Censoring is frequent within psychology but typically unrecognized outside of longitudinal studies. We describe five circumstances when censoring may occur, demonstrate censoring distorts correlations, and discuss how censoring can create spurious factors. Next, we explain how to use R package lava to calculate maximum likelihood estimates (Holst and Budtz-Jørgensen Computational Statistics, 28(4), 1385–1452, 2013) of correlations between uncensored variables based upon censored variables. Previous research demonstrated these estimates were more accurate than Muthén's (1984) estimate for one particular model, but no research has systematically examined their accuracy. We therefore conducted a simulation study exploring the effects of the correlation, sample size, and censoring on point and interval estimates of correlations. Based upon 80 cells in which low values of normally distributed variables were censored, we recommend the constrained regression model with Wald confidence intervals. These methods were precise and unbiased unless both variables had 70% censoring and the correlation was large and negative (e.g., −.9), in which case estimates were closer to −1 than they should be. Opposite results would occur if low values of one variable and high values of the other were censored: Estimates would be precise and unbiased unless censoring was extreme and correlations were large and positive. To estimate large correlations accurately, we recommend researchers reduce censoring by using longer longitudinal studies, using scales with more response options, and matching measures to populations to reduce floor and ceiling effects. [ABSTRACT FROM AUTHOR]

In structural equation modeling, when multiple imputation is used for handling missing data, model fit evaluation involves pooling likelihood-ratio test statistics across imputations. Under the normality assumption, the two most popular pooling approaches were proposed by Li et al. (Statistica Sinica, 1(1), 65–92, 1991) and Meng and Rubin (Biometrika, 79(1), 103–111, 1992). When the assumption of normality is violated, it is not clear how well these pooling approaches work with the test statistics generated from various robust estimators and multiple imputation methods. Jorgensen and colleagues (2021) implemented these pooling approaches in their R package semTools; however, no systematical evaluation has been conducted. In this simulation study, we examine the performance of these approaches in working with different imputation methods and robust estimators under nonnormality. We found that the naïve pooling approach based on Meng and Rubin (Biometrika, 79(1), 103–111, 1992; D3SN) worked the best when combining with the normal-theory-based imputation and either MLM or MLMV estimator. [ABSTRACT FROM AUTHOR]

Recovering missing data and access to a complete and accurate streamflow data is of great importance in water resources management. This article aims to comparatively investigate the application of different classical and machine learning-based methods in recovering missing streamflow data in three mountainous basins in northern Iran using 26 years of data duration extending from 1991 to 2017. These include Taleghan, Karaj, and Latyan basins that provide municipal water for the capital Tehran. Two periods of artificial gaps of data were considered to avoid possible duration-based impacts that may affect the results. For this purpose, several methods are investigated including simple and multiple linear regressions (LR & MLR), artificial neural network (ANN) with five different structures, support vector regression (SVR), M5 tree and two Adaptive Neuro-Fuzzy Inference System (ANFIS) comprising Subtractive (Sub-ANFIS) and fuzzy C-means (FCM-ANFIS) classification. Although these methods have been used in different problems in the past, but the comparison of all these methods and the application of ANFIS using two clustering methods in missing data is new. Overall, it was noticed that machine learning-based methods yield better outputs. For instance, in the Taleghan basin and in the gap during 2014–2017 period it shows that the evaluation criteria of Root Mean Square Error (RMSE), Nash–Sutcliffe Index (NSE) and Coefficient of Determination (R 2) for the Sub-ANFIS method are 1.67 m 3 / s , 0.96 and 0.97, respectively, while these values for the LR are 3.46 m 3 / s , 0.83 and 0.87 respectively. Also, in Latyan basin during the gap of 1991–1994, FCM-ANFIS was found to be the best method to recover the missing monthly flow data with RMSE, NSE and R 2 criteria as 3.17 m 3 / s , 0.88 and 0.92, respectively. In addition, results indicated that using the seasonal index in the artificial neural network model improves the estimations. Finally, a Social Choice (SC) method using the Borda count was employed to evaluate the overall performance of all methods. [ABSTRACT FROM AUTHOR]

Many real-world datasets suffer from the unavoidable issue of missing values, and therefore classification with missing data has to be carefully handled since inadequate treatment of missing values will cause large errors. In this paper, we propose a random subspace sampling method, RSS, by sampling missing items from the corresponding feature histogram distributions in random subspaces, which is effective and efficient at different levels of missing data. Unlike most established approaches, RSS does not train on fixed imputed datasets. Instead, we design a dynamic training strategy where the filled values change dynamically by resampling during training. Moreover, thanks to the sampling strategy, we design an ensemble testing strategy where we combine the results of multiple runs of a single model, which is more efficient and resource-saving than previous ensemble methods. Finally, we combine these two strategies with the random subspace method, which makes our estimations more robust and accurate. The effectiveness of the proposed RSS method is well validated by experimental studies. [ABSTRACT FROM AUTHOR]

Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability. [ABSTRACT FROM AUTHOR]

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method. [ABSTRACT FROM AUTHOR]

Passively-generated location data have the potential to augment mobility and transportation research, as demonstrated by a decade of research. A common trait of these data is a high proportion of missingness. Naïve handling, including list-wise deletion of subjects or days, or linear interpolation across time gaps, has the potential to bias summary results. On the other hand, it is unfeasible to collect mobility data at frequencies high enough to reflect all possible movements. In this paper, we describe the relationship between the temporal and spatial aspects of these data gaps, and illustrate the impact on measures of interest in the field of mobility. We propose a method to deal with missing location data that combines a so-called top-down ratio segmentation method with simple linear interpolation. The linear interpolation imputes missing data. The segmentation method transforms the set of location points to a series of lines, called segments. The method is designed for relatively short gaps, but is evaluated also for longer gaps. We study the effect of our imputation method for the duration of missing data using a completely observed subset of observations from the 2018 Statistics Netherlands travel study. We find that long gaps demonstrate greater downward bias on travel distance, movement events and radius of gyration as compared to shorter but more frequent gaps. When the missingness is unrelated to travel behavior, total sparsity can reach levels of up to 20% with gap lengths of up to 10 min while maintaining a maximum 5% downward bias in the metrics of interest. Temporal aspects can increase these limits; sparsity occurring in the evening or night hours is less biasing due to fewer travel behaviors. [ABSTRACT FROM AUTHOR]

The solution of the distribution system state estimation (DSSE) relies on the presence of physical measurements in real time. Sometimes, these measurements may not reach the control center due to the defects in meter functionality, the large communication time delays, and denial-of-service (DoS) attacks on communication channels. Addressing this issue, a novel deep learning (DL) approach is proposed by using a transformer model with the integration of bi-directional long short-term memory (Bi-LSTM) layer. The proposed model can be leveraged to forecast the unavailable measurement data, which maintains the observability of the system to obtain an accurate solution from DSSE. The superiority of the proposed model is probed by comparing it with other forecasting-based DL models at various percentages of missing measurement data. Further, the effectiveness of the proposed model is evaluated for forecasting the power injections of residential, commercial, and industrial loads as well as renewable energy sources. Finally, the solution of the DSSE is tested with forecasted data and compared with the results of the DSSE for the original measurement data. The simulations are performed on modified IEEE 13-node and IEEE 37-node test systems, and their corresponding results highlighted the superiority of the proposed model in the forecasting of the incomplete data. [ABSTRACT FROM AUTHOR]

Reporting standardized effects in randomized treatment studies aids interpretation and facilitates future meta-analyses and policy considerations. However, when outcome data are missing, achieving an unbiased, accurate estimate of the standardized average treatment effect, sATE, can pose challenges even for those with general knowledge of missing data handling, given that the sATE is a ratio of a mean difference to a (within-group) standard deviation. Under both homogeneity and heterogeneity of variance, a Monte Carlo simulation study was conducted to compare missing data handling strategies in terms of bias and accuracy in the sATE, under specific missingness patterns plausible for randomized pretest posttest studies. Within two broad missing data handling approaches, maximum likelihood and multiple imputation, modeling choices were thoroughly investigated including the analysis model, variance estimator, imputation algorithm, and method of pooling results across imputed datasets. Results demonstrated that although the sATE can be estimated with little bias using either maximum likelihood or multiple imputation, particular attention should be paid to the model and variance estimator, especially at smaller sample sizes (i.e., N = 50). Differences in accuracy were driven by differences in bias. To improve estimation of the sATE in practice, recommendations and a software demonstration are provided. Moreover, a pedagogical explanation of the causes of bias, described separately for the numerator and denominator of the sATE is provided, demonstrating visually how and why bias occurs with certain methods. [ABSTRACT FROM AUTHOR]

We have explored several statistical approaches to impute missing time-to-event data that arise from outcome trials with relatively long follow-up periods. Aligning with the primary estimand, such analyses evaluate the robustness of results by imposing an assumption different from censoring at random (CAR). Although there have been debates over which assumption and which method is more appropriate to be applied to the imputation, we propose to use the collection of retrieved dropouts as the basis of missing data imputation. As retrieved dropouts share a similar disposition, such as treatment discontinuation, with subjects who have missing data, they can reasonably be assumed to characterize the distribution of time-to-event among subjects with missing data. In terms of computational intensity and robustness to violation of underlying distributional assumption, we have compared parametric approaches via MCMC or MLE multivariate sampling procedures to a non-parametric bootstrap approach with respect to baseline hazard function. Each of these approaches follows a process of multiple imputation ("proper imputations"), analysis of complete datasets, and final combination. The type-I error, and power rates are examined under a wide range of scenarios to inform the performance characteristics. A subset of a real unblinded phase III CVOT is used to demonstrate the application of the proposed approaches, compared to the Cox proportional hazards model and jump-to-reference multiple imputation. [ABSTRACT FROM AUTHOR]

Remote sensing is undoubtedly one of the most practical approaches used in many fields such as structural health monitoring. The most notable deal of this method, however, is the data which are either missing or not given by satellites. In this study, an attempt has been made to propose machine learning (ML)-based models for reconstructing missing InSAR data (i.e., buildings' displacement) to monitor their performance more properly. To this end, buildings located in the historical center of Rieti, Rome (Italy), are considered as case studies. Displacement of the points situated on different buildings' roof, given by remote sensing, is utilized for training the relationship between inputs and outputs to the models. The input variables were points' coordinates, height, and soil condition, while the cumulative displacement was the target output. Tree-based techniques namely decision tree, random forest and XGBoost were implemented for developing prediction models. The accuracy of the models was assessed through common performance metrics and Taylor diagram, and the most accurate model was introduced accordingly. The results demonstrated high capability of the tree-based methods for estimating the displacement of buildings. The proposed prediction models could be used for either predicting or reconstructing missing displacement of buildings at any specific point which ease structural performance monitoring remarkably. [ABSTRACT FROM AUTHOR]

Climate change has increased drought frequency globally, which harms the environment, agriculture, and water resources. This study explores the capacity of a hybrid model based on the integration of extreme learning machine (ELM) with a novel meta-heuristic algorithm called Beluga Whale Optimization (BWO) for drought forecasting within tropical region. The forecasting adopted for the standardized streamflow index (SSI) for several time scales (SSI-1, SSI-3, SS1-6, SSI-9, SSI-12, and SSI-24) over Selangor state, Malaysia. The drought calculation was based on 58 years of stream flow data (1961 to 2018) obtained from two hydrological stations, S3615412 and S3414421. The ELM-BWO model was validated with standalone models including Gradient boosting regression (GBR) and classical ELM in addition to the hybridization of ELM with genetic algorithm (GA). The inputs were nominated by using partial autocorrelation function (PACF) at 5% significance level. The research finding reveals that the ELM-BWO model used for the first in hydrological drought forecasting, outperforms other models and provides the narrowest uncertainty bounds. The proposed ELM-BWO model accuracy was demonstrated by the decreased metric of root mean square error (RMSE) values across different drought timescales (e.g., SSI-9 by 12.65% to 78.74%). In addition, the model obtains the highest correlation coefficient (0.9777 and 0.9944) and Wilmot index values (0.9882 and 0.9971) for both stations. The ELM-BWO model can be used to form a dependable expert intelligent system for anticipating hydrological drought at multiple time scales, making decisions about appropriate measures to deal with hydrological drought at the studied stations, and supporting sustainable water resource management. [ABSTRACT FROM AUTHOR]

In recent years, the binomial AR(1) model has been widely used in modeling time series of counts with finite range, such as rainfall prediction, process quality control, research of financial data, disease prevention and control, etc. However, in real-life applications, time series data often exhibit incompleteness missing samples, due to some sensor malfunctions or human errors, such as data input error, measurement error, experimental error or intentional abnormal value etc. In this article, we consider the statistical inference for the binomial AR(1) model with missing data. We first use the conditional least squares and conditional maximum likelihood methods with no imputation (NI) based on incomplete data. Then, we consider the imputation methods. We use the mean imputation, the bridge imputation, and the imputation based on likelihood, of which the last two methods are based on iterative schemes. The performance of the algorithm is shown in the simulation study. Finally, we illustrate our method by presenting a real-data example. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of simultaneously predicting multiple binary responses by utilizing a shared set of covariates. Our approach incorporates machine learning techniques for binary classification, without making assumptions about the underlying observations. Instead, our focus lies on a group of predictors, aiming to identify the one that minimizes prediction error. Unlike previous studies that primarily address estimation error, we directly analyze the prediction error of our method using PAC-Bayesian bounds techniques. In this paper, we introduce a pseudo-Bayesian approach capable of handling incomplete response data. Our strategy is efficiently implemented using the Langevin Monte Carlo method. Through simulation studies and a practical application using real data, we demonstrate the effectiveness of our proposed method, producing comparable or sometimes superior results compared to the current state-of-the-art method. [ABSTRACT FROM AUTHOR]

Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurogenerative condition characterized by sharp cognitive decline with no confirmed effective treatment or cure. This makes it critically important to identify the symptoms of Alzheimer's disease in its early stages before significant cognitive deterioration has taken hold and even before any brain morphology and neuropathology are noticeable. In this study, five different multimodal deep neural networks (MDNN), with different architectures, in search of an optimal model for predicting the cognitive test scores for the Mini-Mental State Examination (MMSE) and the modified Alzheimer's Disease Assessment Scale (ADAS-CoG13) over a span of 60 months (5 years). The multimodal data utilized to train and test the proposed models were obtained from the Alzheimer's Disease Neuroimaging Initiative study and includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from the neuropsychological tests (Cog). The models developed herein delve into two main issues: (1) application merits of single-task vs. multitask for predicting future cognitive scores and (2) whether time-varying input data are better suited than specific timepoints for optimizing prediction results. This model yields a high of 90.27% (SD = 1.36) prediction accuracy (correlation) at 6 months after the initial visit to a lower 79.91% (SD = 8.84) prediction accuracy at 60 months. The analysis provided is comprehensive as it determines the predictions at all other timepoints and all MDNN models include converters in the CN and MCI groups (CNc, MCIc) and all the unstable groups in the CN and MCI groups (CNun and MCIun) that reverted to CN from MCI and to MCI from AD, so as not to bias the results. The results show that the best performance is achieved by a multimodal combined single-task long short-term memory (LSTM) regressor with an input sequence length of 2 data points (2 visits, 6 months apart) augmented with a pretrained Neural Network Estimator to fill in for the missing values. [ABSTRACT FROM AUTHOR]

We describe a method for fitting a Markov chain, with a state transition matrix that depends on a feature vector, to data that can include missing values. Our model consists of separate logistic regressions for each row of the transition matrix. We fit the parameters in the model by maximizing the log-likelihood of the data minus a regularizer. When there are missing values, the log-likelihood becomes intractable, and we resort to the expectation-maximization (EM) heuristic. We illustrate the method on several examples, and describe our efficient Python open-source implementation. [ABSTRACT FROM AUTHOR]

Streamflow missing data rises to a real challenge for calibration and validation of hydrological models as well as for statistically based methods of streamflow prediction. Although several algorithms have been developed thus far to impute missing values of hydro(geo)logical time series, the effectiveness of methods in imputation when the time series are influenced by different seasonalities and variances have remained largely unexplored. Therefore, we evaluated the efficacy of five different statistical algorithms in imputation of streamflow and groundwater level missing data under variegated periodicities and variances. Our performance evaluation is based on the streamflow data, procured from a hydrological model, and the observed groundwater data from the federal state of Brandenburg in Northeast Germany. Our findings revealed that imputations methods embodying the time series nature of the data (i.e., preceding value, autoregressive integrated moving average (ARIMA), and autoregressive conditional heteroscedasticity model (ARCH)) resulted in MSEs (Mean Squared Error) that are between 20 and 40 times smaller than the MSEs obtained from the Ordinary least squares (OLS) regression, which do not consider this quality. ARCH and ARIMA excelled in imputing missing values for hydrological time series, specifically for the streamflow and groundwater level data. ARCH outperformed ARIMA in both the streamflow and groundwater imputation under various conditions, such as without seasonality, with seasonality, low and high variance, and high variance (white noise) conditions. For the streamflow data, ARCH achieved average MSEs of 0.0000704 and 0.0003487 and average NSEs of 0.9957710 and 0.9965222 under without seasonality and high variance conditions, respectively. Similarly, for the groundwater level data, ARCH demonstrated its capability with average MSEs of 0.000635040 and average NSEs of 0.9971351 under GWBR1 condition. The effectiveness of ARCH, originated from econometric time series methods, should be further assessed by other hydro(geo)logical time series obtained from different climate zones. [ABSTRACT FROM AUTHOR]

Missing data cause problems in meteorological, hydrological, and climate analysis. The observation data should be complete and cover long periods to make the research more accurate and reliable. Artificial intelligence techniques have attracted interest for completing incomplete meteorological data in recent years. In this study the abilities of machine learning models, artificial neural networks, the nonlinear autoregressive with exogenous input (NARX) model, support vector regression, Gaussian processes regression, boosted tree, bagged tree (BAT), and linear regression to fill in missing precipitation data were investigated. In developing the machine learning model, 70% of the dataset was used for training, 15% for testing, and 15% for validation. The Bayburt, Tercan, and Zara precipitation stations, which are closest to the Erzincan station and have the highest correlation coefficients, were used to fill the data gaps. The accuracy of the constructed models was tested using various statistical criteria, such as root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe model efficiency coefficient (NSE), and determination coefficient (R2) and graphical approaches such as scattering, box plots, violin plots, and Taylor diagrams. Based on the comparison of model results, it was concluded that the BAT model with R2: 0.79 and NSE: 0.79 and error (RMSE: 11.42, and MAE: 7.93) was the most successful in the completion of missing monthly precipitation data. The contribution of this research is assist in the choice of the best and most accurate method for estimating precipitation data in semi-arid regions like Erzincan. [ABSTRACT FROM AUTHOR]

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Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5–60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window's slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9–2.6 beats/min, respiratory rate: 0.8–1.8 breaths/min, temperature: 0.04–0.17 °C, oxygen saturation: 0.3–0.7% for 5–60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1–8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted. [ABSTRACT FROM AUTHOR]

Data missing has always occurred in data processing. To solve this problem, researchers have improved the process methods of the missing data with diverse strategies, which range from directly deleting missing data samples to using artificial intelligence technology to filling in incomplete data. The processing methods of the missing data have been improved. Generative adversarial imputation network (GAIN) is a kind of neural network which has an excellent performance in missing data imputation. A number of publications that research and cite the GAIN model show a significant growth trend after GAIN was proposed in 2018. GAIN has been studied and improved by many scholars in their specific fields. However, few studies have systematically surveyed the GAIN model's development trends on missing data from its birth to the present, which result in a lack of comprehensive information about GAINs general performance in different fields. In this review, we summarize the development of the GAIN model in missing data imputation from 2018 to 2022. Based on the WOS database, 32 publications are selected according to the PRISMA statement. The outcome of this paper is from the following aspects: (1) analyzing the publication information and application fields quantitatively; (2) expounding the GAIN-based models, classification, and research trends; (3) elaborating the model attributes and missing data mechanism; and (4) summarizing the existing issues and proposing the future directions. Above all, this paper can help scholars gain further insight into the missing data issues and better understand the optimized directions of GAIN models. [ABSTRACT FROM AUTHOR]

Fake news on social media, has spread for personal or societal gain. Detecting fake news is a multi-step procedure that entails analysing the content of the news to assess its trustworthiness. The article has proposed a new solution for fake news detection which incorporates sentiment as an important feature to improve the accuracy with two different data sets of ISOT and LIAR. The key feature words with content's propensity scores of the opinions are developed based on sentiment analysis using a lexicon-based scoring algorithm. Further, the study proposed a multiple imputation strategy which integrated Multiple Imputation Chain Equation (MICE) to handle multivariate missing variables in social media or news data from the collected dataset. Consequently, to extract the effective features from the text, Term Frequency and Inverse Document Frequency (TF-IDF) are introduced to determine the long-term features with the weighted matrix. The correlation of missing data variables and useful data features are classified based on Naïve Bayes, passive-aggressive and Deep Neural Network (DNN) classifiers. The findings of this research described that the overall calculation of the proposed method was obtained with an accuracy of 99.8% for the detection of fake news with the evaluation of various statements such as barely true, half true, true, mostly true and false from the dataset. Finally, the performance of the proposed method is compared with the existing methods in which the proposed method results in better efficiency. [ABSTRACT FROM AUTHOR]

Sparse Gaussian graphical models have been extensively applied to detect the conditional independence structures from fully observed data. However, datasets with missing observations are quite common in many practical fields. In this paper, we propose a robust Gaussian graphical model with the covariance matrix being estimated from the partially observed data. We prove that the inverse of the Karush–Kuhn–Tucker mapping associated with the proposed model satisfies the calmness condition automatically. We also apply a linearly convergent alternating direction method of multipliers to find the solution to the proposed model. The numerical performance is evaluated on both the synthetic data and real data sets. [ABSTRACT FROM AUTHOR]