Search

Diabetes is a chronic disease, which is characterized by abnormally high blood sugar levels. It may affect various organs and tissues, and even lead to life-threatening complications. Accurate prediction of diabetes can significantly reduce its incidence. However, the current prediction methods struggle to accurately capture the essential characteristics of nonlinear data, and the black-box nature of these methods hampers its clinical application. To address these challenges, we propose KCCAM_DNN, a diabetes prediction method that integrates Kendall's correlation coefficient and an attention mechanism within a deep neural network. In the KCCAM_DNN, Kendall's correlation coefficient is initially employed for feature selection, which effectively filters out key features influencing diabetes prediction. For missing values in the data, polynomial regression is utilized for imputation, ensuring data completeness. Subsequently, we construct a deep neural network (KCCAM_DNN) based on the self-attention mechanism, which assigns greater weight to crucial features affecting diabetes and enhances the model's predictive performance. Finally, we employ the SHAP model to analyze the impact of each feature on diabetes prediction, augmenting the model's interpretability. Experimental results show that KCCAM_DNN exhibits superior performance on both PIMA Indian and LMCH diabetes datasets, achieving test accuracies of 99.090% and 99.333%, respectively, approximately 2% higher than the best existing method. These results suggest that KCCAM_DNN is proficient in diabetes prediction, providing a foundation for informed decision-making in the diagnosis and prevention of diabetes. [ABSTRACT FROM AUTHOR]

The site regression model (SREG) is utilized by plant breeders for analyzing multi-environment trials (MET) to examine the relationships among test environments, genotypes (G), and genotype-by-environment interactions (GE). SREG explores a matrix of G and GE by performing a singular value decomposition on the residuals matrix from a one-way ANOVA, requiring complete data. As missing values are common in MET, we propose two new imputation methods that implement an Expectation Maximization algorithm to fit the SREG model. To evaluate the impact on SREG model parameter estimation of these proposed methods and other competing imputation methods available, we conducted two studies using different approaches. One study involved simulated data while the other used a real dataset. In both studies, different measures to verify whether the joint effect of G plus GE is altered by imputation of data, and the reproducibility of missing data were evaluated. We also incorporated situations not commonly addressed in the literature, such as non-random structures of missing values and a big data situation. The proposed procedures provided estimators with good performance, maintaining superiority in several aspects studied, even when the competing imputation methods did not achieve convergence. Therefore, the new methods enabled incomplete MET data to be effectively analyzed by a SREG model. [ABSTRACT FROM AUTHOR]

Imputation of missing data in high-dimensional datasets with more variables P than samples N, P ≫ N , is hampered by the data dimensionality. For multivariate imputation, the covariance matrix is ill conditioned and cannot be properly estimated. For fully conditional imputation, the regression models for imputation cannot include all the variables. Thus, the high dimension requires special imputation approaches. In this article, we provide an overview and realistic comparisons of imputation approaches for high-dimensional data when applied to a linear mixed modeling (LMM) framework. We examine approaches from three different classes using simulation studies: multiple imputation with penalized regression, multiple imputation with recursive partitioning and predictive mean matching; and multiple imputation with Principal Component Analysis (PCA). We illustrate the methods on a real case study where a multivariate outcome (i.e., an extracted set of correlated biomarkers from human urine samples) was collected and monitored over time and we discuss the proposed methods with more standard imputation techniques that could be applied by ignoring either the multivariate or the longitudinal dimension. Our simulations demonstrate the superiority of the recursive partitioning and predictive mean matching algorithm over the other methods in terms of bias, mean squared error and coverage of the LMM parameter estimates when compared to those obtained from a data analysis without missingness, although it comes at the expense of high computational costs. It is worthwhile reconsidering much faster methodologies like the one relying on PCA. [ABSTRACT FROM AUTHOR]

The identification of tumor-specific molecular dependencies is essential for the development of effective cancer therapies. Genetic and chemical perturbations are powerful tools for discovering these dependencies. Even though chemical perturbations can be applied to primary cancer samples at large scale, the interpretation of experiment outcomes is often complicated by the fact that one chemical compound can affect multiple proteins. To overcome this challenge, Batzilla et al. (PLoS Comput Biol 18(8): e1010438, 2022) proposed DepInfeR, a regularized multi-response regression model designed to identify and estimate specific molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles. Inspired by their work, we propose a Bayesian extension to DepInfeR. Our proposed approach offers several advantages over DepInfeR, including e.g. the ability to handle missing values in both protein-drug affinity and drug sensitivity profiles without the need for data pre-processing steps such as imputation. Moreover, our approach uses Gaussian Processes to capture more complex molecular dependency structures, and provides probabilistic statements about whether a protein in the protein-drug affinity profiles is informative to the drug sensitivity profiles. Simulation studies demonstrate that our proposed approach achieves better prediction accuracy, and is able to discover unreported dependency structures. [ABSTRACT FROM AUTHOR]

Statistical approaches that successfully combine multiple datasets are more powerful, efficient, and scientifically informative than separate analyses. To address variation architectures correctly and comprehensively for high-dimensional data across multiple sample sets (ie, cohorts), we propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization method to concurrently learn both covariate-driven and auxiliary structured variations. We consider a structured nuclear norm objective that is motivated by random matrix theory, in which the regression or factorization terms may be shared or specific to any number of cohorts. Our framework subsumes several existing methods, such as reduced rank regression and unsupervised multimatrix factorization approaches, and includes a promising novel approach to regression and factorization of a single dataset (aRRR) as a special case. Simulations demonstrate substantial gains in power from combining multiple datasets, and from parsimoniously accounting for all structured variations. We apply maRRR to gene expression data from multiple cancer types (ie, pan-cancer) from The Cancer Genome Atlas, with somatic mutations as covariates. The method performs well with respect to prediction and imputation of held-out data, and provides new insights into mutation-driven and auxiliary variations that are shared or specific to certain cancer types. [ABSTRACT FROM AUTHOR]

Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an "oracle," can be constructed in such cases to decide the best procedure from a set C$$ \mathcal{C} $$ of available preparatory procedures. The oracle learns the decision regions associated with C$$ \mathcal{C} $$ based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit. [ABSTRACT FROM AUTHOR]

Background: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. Methods: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. Results: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. Conclusions: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data. [ABSTRACT FROM AUTHOR]

Linear regression models are commonly used to determine the quantitative relationships between variables and utilize the resulting regression equations to make predictions. This paper proposes a fully informative multiple imputation method based on a linear regression model with a missing response variable, utilizing all observable data to obtain estimates of the regression coefficients and thereby the predicted values of the missing response variable. This not only provides a good explanation of the relationship between the response variable and their respective variables, but also effectively enhances the imputation accuracy of the response variable. The stability and sensitivity of the fiMI method are evaluated through a simulation study. Subsequently, the proposed method is applied to two real data sets, the admission prediction data set and the goalkeeper data set, and is discussed and analyzed. [ABSTRACT FROM AUTHOR]

There is a growing need for flexible general frameworks that integrate individual‐level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation‐based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population. [ABSTRACT FROM AUTHOR]

Missing data is common in survival analysis. It is either removed or imputed using various methods. Expectation-maximization (EM) imputation is a popular method in Cox regression studies. This paper investigated the effect of different regression methods on Cox regression modeling within the framework of EM. A stratified Cox regression model was derived from a dataset of categorical and numerical variables. Missing data were imputed using the EM framework with five machine learning algorithms and then were compared to the full model. The results show that the recursive partition and regression tree (RPART) method performed better than others. However, all regression methods performed poorly in categorical covariate imputation. R code is available online. [ABSTRACT FROM AUTHOR]

Modern science is frequently based on the exploitation of large volumes of information storage in datasets and involving complex computational architectures. The statistical analyses of these datasets have to cope with specific challenges and frequently involve making informed but arbitrary decisions. Epidemiological papers have to be concise and focused on the underlying clinical or epidemiological results, not reporting the details behind relevant methodological decisions. In this work, we used an analysis of the cardiovascular-related measures tracked in 4–8-year-old children, using data from the INMA-Asturias cohort for illustrating how the decision-making process was performed and its potential impact on the obtained results. We focused on two particular aspects of the problem: how to deal with missing data and which regression model to use to evaluate tracking when there are no defined thresholds to categorize variables into risk groups. As a spoiler, we analyzed the impact on our results of using multiple imputation and the advantage of using quantile regression models in this context. [ABSTRACT FROM AUTHOR]

It is not uncommon for biological anthropologists to analyze incomplete bioarcheological or forensic skeleton specimens. As many quantitative multivariate analyses cannot handle incomplete data, missing data imputation or estimation is a common preprocessing practice for such data. Using William W. Howells' Craniometric Data Set and the Goldman Osteometric Data Set, we evaluated the performance of multiple popular statistical methods for imputing missing metric measurements. Results indicated that multiple imputation methods outperformed single imputation methods, such as Bayesian principal component analysis (BPCA). Multiple imputation with Bayesian linear regression implemented in the R package norm2, the Expectation–Maximization (EM) with Bootstrapping algorithm implemented in Amelia, and the Predictive Mean Matching (PMM) method and several of the derivative linear regression models implemented in mice, perform well regarding accuracy, robustness, and speed. Based on the findings of this study, we suggest a practical procedure for choosing appropriate imputation methods. [ABSTRACT FROM AUTHOR]

Navigating the intricate world of data analytics, one method has emerged as a key tool in confronting missing data: multiple imputation. Its strength is further fortified by its powerful variant, robust imputation, which enhances the precision and reliability of its results. In the challenging landscape of data analysis, non-robust methods can be swayed by a few extreme outliers, leading to skewed imputations and biased estimates. This can apply to both representative outliers—those true yet unusual values of your population—and non-representative outliers, which are mere measurement errors. Detecting these outliers in large or high-dimensional data sets often becomes as complex as unraveling a Gordian knot. The solution? Turn to robust imputation methods. Robust (imputation) methods effectively manage outliers and exhibit remarkable resistance to their influence, providing a more reliable approach to dealing with missing data. Moreover, these robust methods offer flexibility, accommodating even if the imputation model used is not a perfect fit. They are akin to a well-designed buffer system, absorbing slight deviations without compromising overall stability. In the latest advancement of statistical methodology, a new robust imputation algorithm has been introduced. This innovative solution addresses three significant challenges with robustness. It utilizes robust bootstrapping to manage model uncertainty during the imputation of a random sample; it incorporates robust fitting to reinforce accuracy; and it takes into account imputation uncertainty in a resilient manner. Furthermore, any complex regression or classification model for any variable with missing data can be run through the algorithm. With this new algorithm, we move one step closer to optimizing the accuracy and reliability of handling missing data. Using a realistic data set and a simulation study including a sensitivity analysis, the new alogorithm imputeRobust shows excellent performance compared with other common methods. Effectiveness was demonstrated by measures of precision for the prediction error, the coverage rates, and the mean square errors of the estimators, as well as by visual comparisons. [ABSTRACT FROM AUTHOR]

Multivariate time series measurements in plasma diagnostics present several challenges when training machine learning models: the availability of only a few labeled data increases the risk of overfitting, and missing data points or outliers due to sensor failures pose additional difficulties. To overcome these issues, we introduce a fast and robust regression model that enables imputation of missing points and data augmentation by massive sampling while exploiting the inherent correlation between input signals. The underlying Student‐t process allows for a noise distribution with heavy tails and thus produces robust results in the case of outliers. We consider the state space form of the Student‐t process, which reduces the computational complexity and makes the model suitable for high‐resolution time series. We evaluate the performance of the proposed method using two test cases, one of which was inspired by measurements of flux loop signals. [ABSTRACT FROM AUTHOR]

Dealing with missing values is an important issue in data observation or data recording process. In this paper, we consider a functional linear regression model with partially observed covariate and missing values in the response. We use a reconstruction operator that aims at recovering the missing parts of the explanatory curves, then we are interested in regression imputation method of missing data on the response variable, using functional principal component regression to estimate the functional coefficient of the model. We study the asymptotic behaviour of the prediction error when missing data are replaced by the imputed values in the original dataset. The practical behaviour of the method is also studied on simulated data and a real dataset. [ABSTRACT FROM AUTHOR]

Fully conditional specification (FCS) is a convenient and flexible multiple imputation approach. It specifies a sequence of simple regression models instead of a potential complex joint density for missing variables. However, FCS may not converge to a stationary distribution. Many authors have studied the convergence properties of FCS when priors of conditional models are non-informative. We extend to the case of informative priors. This paper evaluates the convergence properties of the normal linear model with normal-inverse gamma priors. The theoretical and simulation results prove the convergence of FCS and show the equivalence of prior specification under the joint model and a set of conditional models when the analysis model is a linear regression with normal inverse-gamma priors. [ABSTRACT FROM AUTHOR]

• This study examined the associations between spanking experiences and children's subsequent reading and math scores. • Entropy balance matching was employed based on lifetime and recent incidence of spanking. • Lifetime experience of spanking by age 5 did not predict children's subsequent reading and math scores. • A recent incidence of spanking at the age of 5 was significantly associated with lower reading and math scores at ages 6 and 7, compared with those who had been spanked but not recently. Using the Early Childhood Longitudinal Study – Kindergarten Class of 2010–2011, this study examined the associations between spanking experiences and children's subsequent reading and math scores. Two different groups were matched on a range of family sociodemographics and children's initial reading and math scores using entropy balancing: not spanked vs spanked (Ns=17,171–17,537); and not recently vs recently spanked (Ns=10,393–10,724). Sample sizes were provided in ranges because they varied across multiple imputed data sets. The results of the lagged dependent variable regression analyses showed that a lifetime experience of spanking by age 5 did not predict children's subsequent reading and math scores. However, children who were recently spanked at the age of 5 showed significantly lower reading and math scores at ages 6 and 7 compared with those who were spanked but not recently. These results, replicated using different estimation methods (i.e., difference score analyses), strengthen the argument that spanking may impair early reading and math skills. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in general applied studies, and specially in clinical trials. For implementing sensitivity analysis, several multiple imputation methods exist, like sequential imputation, which restricts to monotone missingness, and Bayesian, where the imputation and analysis models differ, entailing overestimation of variance. Also, full conditional specification provides a conditional interpretation of sensitivity parameters, requiring further calibration to get the desired marginal interpretation. We propose in this paper a multiple imputation procedure, based on a multivariate linear regression model, which keeps compatibility in sensitivity analysis under intermittent missingness, providing a marginal interpretation of the elicited parameters. Simulation studies show that the method behaves well with longitudinal data and remains robust under demanding constraints. We conclude the possibility of situations not covered by the existing methods and well suited for our proposal, which allows more efficient handling of a given multivariate linear regression structure. Its use is illustrated in a real case study, where a sensitivity analysis is accomplished. [ABSTRACT FROM AUTHOR]

Missing data is a common problem in real data analysis. In this paper, a Mallows model averaging method based on kernel regression imputation is proposed for the linear regression models with responses missing at random. We prove that our method asymptotically achieves the lowest possible squared error. Compared with the existing model averaging methods, the new method does not require the use of a parameter model to characterize the missing generation mechanism. The Monte Carlo simulation and a practical application demonstrate the usefulness of the proposed method. • Mallows model averaging method based on kernel regression imputation. • Asymptotic optimality in the sense of achieving the lowest possible squared error. • There is no need to use a parametric model to characterize the missing generation mechanism. [ABSTRACT FROM AUTHOR]

• We develop a methodological framework to correct missingness in heterogeneous mobile data using a multi-task GP model. The resulting (repaired) data can be used for many transportation and mobility applications downstream. Our model considers correlations between users' coordinates and adjusts for individual-level differences in data characteristics; • We analyze our model's key hyperparameters (e.g., lengthscale parameters) and imputation accuracy concerning different types of trips (e.g. walking trips vs. highway trips). Specifically, we show the linkage between trip types and lengthscale parameters; • We introduce and demonstrate the combined use of smooth kernels (i.e., the rational quadratic kernel) and kernels with periodicities (i.e., the periodic kernel) in modeling human mobility data; and. • We benchmark our model against other missing data imputation methods, demonstrating its effectiveness under a range of missingness scenarios (short and long gaps). The prevalence of mobile devices and the ubiquity of network connectivity have generated a massive amount of temporally- and spatially-stamped data. A key characteristic of this mobile data is the prevalence of sparsity—for every recorded user, there is a significant amount of missing data. Sparsity leads to bias in the inferred mobility patterns and thus, correcting bias by imputing the missing data potentially creates opportunities for directly using the corrected mobile trajectory data for large-scale simulations in various applications. We propose a multi-task Gaussian process regression model to correct missingness in mobile data. Gaussian processes (GPs) allow for flexible modeling of diverse (i.e., non-linear, locally periodic) data patterns and quantify prediction uncertainty in an interpretable manner. We develop a methodological framework for applying GPs to mobile data. In doing so, we consider the correlations between users' coordinates (latitudes and longitudes) through multi-task learning and adjust for individual-level differences in data characteristics through parameter initialization and optimization. We introduce and demonstrate the use of smooth (i.e., rational quadratic) and periodic kernels in modeling human mobility data. Relatedly, we analyze our model's parameters and imputation accuracy with respect to different types of trips (e.g. slower vs faster trips). We also demonstrate our model's performance in two experiments with real app-based data, in which it outperforms alternative imputation methods. Our implementation is open-source at https://github.com/ekinugurel/GPSImpute. [ABSTRACT FROM AUTHOR]

Missing data is a prevailing problem in predictive analytics. In this paper, a winner‐take‐all (WTA) autoencoder‐based piecewise linear model is developed to solve the nonlinear regression problem under the missing value scenario, which consists of two parts: an overcomplete WTA autoencoder and a gated linear network. The overcomplete WTA autoencoder is a stacked denoising autoencoder (SDAE) designed to play two roles: (1) to estimate the missing values; (2) to realize a sophisticated partitioning by generating a broad set of binary gate control sequences. Besides, an iterative algorithm with renewed teacher signals is developed to train the SDAE. On the other hand, the gated linear network with the generated binary gate control sequences implements a flexible piecewise linear model for nonlinear regression. By composing a quasi‐linear kernel based on the gate control sequences, the piecewise linear model is then identified in the same way as a support vector regression. Experimental results have shown that our proposed hybrid model has a better performance than traditional models. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant. [ABSTRACT FROM AUTHOR]

The primary analysis of time‐to‐event data typically makes the censoring at random assumption, that is, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved. In such cases, we need to explore the robustness of inference to more pragmatic assumptions about patients post‐censoring in sensitivity analyses. Reference‐based multiple imputation, which avoids analysts explicitly specifying the parameters of the unobserved data distribution, has proved attractive to researchers. Building on results for longitudinal continuous data, we show that inference using a Tobit regression imputation model for reference‐based sensitivity analysis with right censored log normal data is information anchored, meaning the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. We illustrate our theoretical results using simulation and a clinical trial case study. [ABSTRACT FROM AUTHOR]

In this article, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in order to confirm the theoretical results. The authors provide exact test procedures, which in case multiple synthetic datasets are permissible, are compared with the asymptotic results of Reiter. An application using 2000 U.S. Current Population Survey public use data is discussed. Furthermore, properties of the proposed methodology are evaluated in scenarios where some of the conditions that were used to derive the methodology do not hold, namely for nonnormal and discrete distributed random variables, cases in which the inferential procedures developed still show very good performances. [ABSTRACT FROM AUTHOR]

Missing data were frequently found in the instrumental climatic records, which hindered the statistical analyses on climate change. A novel imputation method, called Imputation Based on Decomposition of Time Series (IBDTS), was developed in this article for the climatic data with strong seasonality and spatial correlation. It was to decompose the time series into three components first, and then to predict the missing values in each component. The trend component was predicted by regression analysis, the seasonal component was predicted by spectral analysis, and the remainder component was predicted by spatial interpolation. The IBDTS imputation method showed relatively small errors in performance, and kept the real attributes of climatic series, including the amplitude and phase with the cycle period of 12 months, and the linear trend. The sensibility to station distance for the IBDTS method was relatively small. In addition, the IBDTS method had the ability to deal with the data with none of or only a few of complete series, and it was possible to be applied not only in the field of climatology but also in other fields as long as the data had the intrinsic properties of strong seasonality and spatial correlation. [ABSTRACT FROM AUTHOR]

Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We describe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four. [ABSTRACT FROM AUTHOR]

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Presence of detection limit (DL) in covariates causes inflated bias and inaccurate mean squared error to the estimators of the regression parameters. This paper suggests a response-driven multiple imputation method to correct the deleterious impact introduced by the covariate DL in the estimators of the parameters of simple logistic regression model. The performance of the method has been thoroughly investigated, and found to outperform the existing competing methods. The proposed method is computationally simple and easily implementable by using three existing R libraries. The method is robust to the violation of distributional assumption for the covariate of interest. [ABSTRACT FROM AUTHOR]

We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Missing values are a common occurrence in condition monitoring datasets. To effectively improve the integrity of data, many data imputation methods have been developed to replace the missing values with the estimated values. However, these methods do not always perform well in datasets containing different types of missing values. Three types of missing data are defined, namely isolated missing value, continuous missing variable, and continuous missing sample. A three-step data imputation method is proposed to sequentially impute these missing values following the principle from easy to difficult. The original time series data is first to split into different segments according to the positions of continuous missing samples. Then, interpolation and space-based methods are applied to sequentially estimate isolated missing values and continuous missing variables in each segment. Finally, a stepwise extrapolation prediction model based on the long short-term memory network is established to repair continuous missing samples between each segment. Two application examples are implemented on different dissolved gas analysis datasets and load datasets. Compared with state-of-the-art techniques, the proposed three-step data imputation method is general and can be applied to many scenarios because it establishes a rational data recovery sequence to accurately repair both stationary and non-stationary condition monitoring data. [ABSTRACT FROM AUTHOR]

This article addresses issues in creating public‐use data files in the presence of missing ordinal responses and subsequent statistical analyses of the dataset by users. The authors propose a fully efficient fractional imputation (FI) procedure for ordinal responses with missing observations. The proposed imputation strategy retrieves the missing values through the full conditional distribution of the response given the covariates and results in a single imputed data file that can be analyzed by different data users with different scientific objectives. Two most critical aspects of statistical analyses based on the imputed data set, validity and efficiency, are examined through regression analysis involving the ordinal response and a selected set of covariates. It is shown through both theoretical development and simulation studies that, when the ordinal responses are missing at random, the proposed FI procedure leads to valid and highly efficient inferences as compared to existing methods. Variance estimation using the fractionally imputed data set is also discussed. The Canadian Journal of Statistics 48: 138–151; 2020 © 2019 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Zero-inflated Poisson (ZIP) regression is widely applied to model effects of covariates on an outcome count with excess zeros. In some applications, covariates in a ZIP regression model are partially observed. Based on the imputed data generated by applying the multiple imputation (MI) schemes developed by Wang and Chen (Ann Stat 37:490–517, 2009), two methods are proposed to estimate the parameters of a ZIP regression model with covariates missing at random. One, proposed by Rubin (in: Proceedings of the survey research methods section of the American Statistical Association, 1978), consists of obtaining a unified estimate as the average of estimates from all imputed datasets. The other, proposed by Fay (J Am Stat Assoc 91:490–498, 1996), consists of averaging the estimating scores from all imputed data sets to solve the imputed estimating equation. Moreover, it is shown that the two proposed estimation methods are asymptotically equivalent to the semiparametric inverse probability weighting method. A modified formula is proposed to estimate the variances of the MI estimators. An extensive simulation study is conducted to investigate the performance of the estimation methods. The practicality of the methodology is illustrated with a dataset of motorcycle survey of traffic regulations. [ABSTRACT FROM AUTHOR]

Item non-response in surveys is usually handled by single imputation, whose main objective is to reduce the non-response bias. Imputation methods need to be adapted to the study variable. For instance, in business surveys, the interest variables often contain a large number of zeros. Motivated by a mixture regression model, we propose two imputation procedures for such data and study their statistical properties. We show that these procedures preserve the distribution function if the imputation model is well specified. The results of a simulation study illustrate the good performance of the proposed methods in terms of bias and mean square error. • Imputation needs to be adapted for zero-inflated data. • A proposed method leads to consistent estimators and preserves distribution. • We propose a balanced imputation method which is fully efficient. [ABSTRACT FROM AUTHOR]

There is a rich literature on the analysis of longitudinal data with missing values. However, the analysis becomes complex for semi-continuous (zero-inflated) longitudinal response with missingness. In this article, we propose a partially varying coefficients regression model for analysing such data. We use a two-part model, where in the first part we propose a latent dynamic model for accounting a 'zero' or a 'non-zero' response, and in the second part we use another dynamic model for estimating the mean trajectories of non-zero responses. Two dynamic models are linked through subject-specific random effects. The missing covariates are imputed repeatedly based on their respective posterior predictive distributions and the missing responses are imputed using the working model under different identifying restrictions. We analyse data from the Health and Retirement Study (HRS) for aged individuals and develop a dynamic model for predicting out-of-pocket medical expenditures (OOPME) containing excess zeros. The operating characteristics of the proposed model are investigated through extensive simulation studies. [ABSTRACT FROM AUTHOR]

We present a new method to estimate individual knot volumes based on a knot geometry model coupled with observations on branch characteristics. X-ray computer tomography and image analysis were used to measure the volume and geometry of 424 knots of Sitka spruce (Picea sitchensis (Bong.) Carrière). Knot geometry can be described mathematically by deriving functions for relative vertical position, diameter, and slope dependent on radial position in the stem. These functions were parameterized using "seemingly unrelated regression" and mixed-modelling techniques. This provided a base model for typical knots. To estimate individual knot volume, we used available data for branch diameter and insertion angle to obtain conditional predictions. We imputed the most likely knot trajectory, as relative vertical position cannot be measured on branches. The model explained up to 96% of the variability in knot volume by incorporating the branch measurements, in contrast to the 43% explained using the typical knot model. Knot volume assessment based only on conditional predictions of diameter and marginal predictions of vertical position also accounted for 96% of the variability. Therefore, measurements of branch diameter alone would be enough to obtain highly precise predictions of individual knot volume. This estimator is a first step towards a knot model to be used for the management of Sitka spruce in Great Britain. [ABSTRACT FROM AUTHOR]

We consider the situation where there is a known regression model that can be used to predict an outcome, Y, from a set of predictor variables X. A new variable B is expected to enhance the prediction of Y. A dataset of size n containing Y,X and B is available, and the challenge is to build an improved model for Y|X,B that uses both the available individual level data and some summary information obtained from the known model for Y|X. We propose a synthetic data approach, which consists of creating m additional synthetic data observations, and then analyzing the combined dataset of size n + m to estimate the parameters of the Y|X,B model. This combined dataset of size n + m now has missing values of B for m of the observations, and is analyzed using methods that can handle missing data (e.g., multiple imputation). We present simulation studies and illustrate the method using data from the Prostate Cancer Prevention Trial. Though the synthetic data method is applicable to a general regression context, to provide some justification, we show in two special cases that the asymptotic variances of the parameter estimates in the Y|X,B model are identical to those from an alternative constrained maximum likelihood estimation approach. This correspondence in special cases and the method's broad applicability makes it appealing for use across diverse scenarios. The Canadian Journal of Statistics 47: 580–603; 2019 © 2019 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this article, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study. for this article are available online. [ABSTRACT FROM AUTHOR]

• A Bayesian vector auto-regression data analytics approach is proposed. • A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved variables. • The model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. Traffic collision data are always collected in irregularly spaced and mixed frequency. Conventional treatment on these kinds of data, for instance, aggregating the high-frequency data into the lower frequency, can lead to the loss of relevant information of high-frequency data, and introduce potential temporal instabilities. A novel Bayesian vector autoregression approach is proposed to address this problem. An unevenly-spaced traffic collision data with missing values, containing all collisions in different severities that occurred on the state highways in Washington State from January 2006 to December 2016, is selected in this study the impacts of transportation-, weather- and socioeconomic-related characteristics on traffic collisions. A Gibbs sampler is used to conduct Bayesian inference for model parameters and unobserved high-frequency variables. Results show that the model has a fairly superior fit accuracy, and is able to capture the unobserved heterogeneity in the dataset. The proposed VAR also demonstrates better performance than other missing value imputation techniques, including linear regression, predictive mean matching, k-nearest neighbors, and random forests. This study provides potential in the guidance of model construction that considers the mixed-time-series nature of data. [ABSTRACT FROM AUTHOR]

Purpose: In several biomedical studies, one or more exposures of interest may be subject to nonrandom missingness because of the failure of the measurement assay at levels below its limit of detection. This issue is commonly encountered in studies of the metabolome using tandem mass spectrometry-based technologies. Owing to a large number of metabolites measured in these studies, preserving statistical power is of utmost interest. In this article, we evaluate the small sample properties of the missing indicator approach in logistic and conditional logistic regression models.Methods: For nested case-control or matched case control study designs, we evaluate the bias, power, and type I error associated with the missing indicator method using simulation. We compare the missing indicator approach to complete case analysis and several imputation approaches.Results: We show that under a variety of settings, the missing indicator approach outperforms complete case analysis and other imputation approaches with regard to bias, mean squared error, and power.Conclusions: For nested case-control and matched study designs of modest sample sizes, the missing indicator model minimizes loss of information and thus provides an attractive alternative to the oft-used complete case analysis and other imputation approaches. [ABSTRACT FROM AUTHOR]

• Multiple imputations (MI) is used to deal with missing data. • Two new approaches are taken, yielding similar results for the data at hand. • Simulations are used to try to understand how many MIs are needed. • Gaussian mixture modeling (GMM) is used for cluster analysis. • Polynomial regression models are used for understanding cluster drivers. The analysis of French tomato data from a 2004 Sensometrics workshop is revisited. The workshop posed two questions (1) are there consumer segments in hedonic data and (2) if there are segments, can they be characterized using consumer and tomato attributes. The challenge with the hedonic data is a large amount of missing data. "Probabilistic" solutions to the latter via multiple imputation are explored. In addition to more traditional methods, polynomial models are used to answer the second question. [ABSTRACT FROM AUTHOR]

Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis. Objective of this research paper is to provide better understanding of data missingness mechanism, data imputation methods, and to assess performance of the widely used data imputation methods for numeric dataset. It will help practitioners and data scientists to select appropriate method of data imputation for numeric dataset while performing data mining task. In this paper, we comprehensively compare seven data imputation methods namely mean imputation, median imputation, kNN imputation, predictive mean matching, Bayesian Linear Regression (norm), Linear Regression, non-Bayesian (norm.nob), and random sample. We have used five different numeric datasets obtained from UCI machine learning repository for analyzing and comparing performance of the data imputation methods. Performance of the data imputation methods is assessed using Normalized Root Mean Square Error (RMSE) method. The results of analysis show that kNN imputation method outperforms the other methods. It has also been found that performance of the data imputation method is independent of the dataset and percentage of missing values in the dataset. [ABSTRACT FROM AUTHOR]

Background: Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situations.Methods: We provide guidance on choice of analysis when data are incomplete. Using causal diagrams to depict missingness mechanisms, we describe when CCA will not be biased by missing data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data situations. We illustrate selection of an appropriate method in practice.Results: For most regression models, CCA gives unbiased results when the chance of being a complete case does not depend on the outcome after taking the covariates into consideration, which includes situations where data are missing not at random. Consequently, there are situations in which CCA analyses are unbiased while MI analyses, assuming missing at random (MAR), are biased. By contrast MI, unlike CCA, is valid for all MAR situations and has the potential to use information contained in the incomplete cases and auxiliary variables to reduce bias and/or improve precision. For this reason, MI was preferred over CCA in our real data example.Conclusions: Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information. [ABSTRACT FROM AUTHOR]

• Identified factors affecting imputation accuracy and examined effects on various methods. • Developed a robust imputation model using dynamic weighting based on missingness scenarios. • Experiments on real-world water-quality data show superior accuracy of proposed method. • Proposed method's robustness highlighted by outperforming baselines in various scenarios. Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data. [Display omitted] [ABSTRACT FROM AUTHOR]

• Selection of predictor variables is a key step in airborne laser scanning (ALS). • Co-registration errors alter the relationship between ground and ALS data. • Parametric modeling was compared to kNN imputation based on Random Forests (RF). • RF-based selection was more affected by errors compared to the parametric case. • The performance of models for dominant height were the best in both approaches The estimation of forest attributes using active remote sensing data and sampling designs is a solid scientific discipline oriented to derive structural information from forest ecosystems. In this study, we examined the problem of selecting predictor variables derived from airborne laser scanning (ALS) when using stepwise and multiple linear regression, and when using kNN imputation using Random Forests (RF) to select predictor variables. Systems of forest attributes and ecological indicators were estimated using ALS statistics and ground data collected in Central-East Spain, on two Mediterranean pine forest areas. Co-registration errors were simulated by means of altering ground data coordinates. The larger the co-registration error, the higher the variation in the selected ALS-based predictors in the parametric approach, which was less affected than the RF-based selection. In both cases, the final performance of the models was not as affected by co-registration errors as the observed variation in the selection of variables: the impact was around 1% in the parametric approach while the value reached the 8% for both stand basal area and the Gini-index under the non-parametric method. The forest biophysical attributes of the set behaved differently towards altering the co-registration factor in both cases. To control the co-registration factor between remote sensing data sources and ground surveys is an essential step to reduce uncertainty in data interpretation and in the modeling of forest attributes. [ABSTRACT FROM AUTHOR]

We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to non-parametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression, and the predictive mean matching imputation (PMM) method. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the PMM method is sensitive to misspecification of the covariates included in imputation. The AIPW method is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program. [ABSTRACT FROM AUTHOR]

Missing data is a common issue that can cause problems in estimation and inference in biomedical, epidemiological and social research. Multiple imputation is an increasingly popular approach for handling missing data. In case of a large number of covariates with missing data, existing multiple imputation software packages may not work properly and often produce errors. We propose a multiple imputation algorithm called mispr based on sequential penalized regression models. Each variable with missing values is assumed to have a different distributional form and is imputed with its own imputation model using the ridge penalty. In the case of a large number of predictors with respect to the sample size, the use of a quadratic penalty guarantees unique estimates for the parameters and leads to better predictions than the usual Maximum Likelihood Estimation (MLE), with a good compromise between bias and variance. As a result, the proposed algorithm performs well and provides imputed values that are better even for a large number of covariates with small samples. The results are compared with the existing software packages mice, VIM and Amelia in simulation studies. The missing at random mechanism was the main assumption in the simulation study. The imputation performance of the proposed algorithm is evaluated with mean squared imputation error and mean absolute imputation error. The mean squared error ( β^ ), parameter estimates with their standard errors and confidence intervals are also computed to compare the performance in the regression context. The proposed algorithm is observed to be a good competitor to the existing algorithms, with smaller mean squared imputation error, mean absolute imputation error and mean squared error. The algorithm's performance becomes considerably better than that of the existing algorithms with increasing number of covariates, especially when the number of predictors is close to or even greater than the sample size. Two real-life datasets are also used to examine the performance of the proposed algorithm using simulations. [ABSTRACT FROM AUTHOR]

Introduction: Behavioral Activation is a behavioral-based treatment that has been proposed as suitable for smoking cessation, as it simultaneously addresses reinforcement-related variables and also mood management. The aim of this study was to compare the effects of a cognitive-behavioral smoking cessation treatment with components of behavioral activation (SCBSCT-BA) with a standard cognitive-behavioral treatment (SCBSCT), and a wait-list control group (WL). Method: The sample was comprised of 275 adults smokers (61.4% females, mean age = 45.36, SD = 10.96). After baseline assessment sessions, participants were randomized (ratio: 2.2.1.) to SCBSCT-BA, SCBSCT, or WL. Active groups received 8 weekly 1-hour face-to-face group sessions. Biochemically verified smoking abstinence and depressive symptoms were assessed at the end of treatment, and at 3-, 6-, and 12-month follow-ups. Results: Significant treatment effects in 7-dayspoint prevalence abstinence rates were found for both active groups at the end of treatment. Abstinence rates at 12-months follow-up were 30% for SCBSCT-BA, and 18% for SCBSCT. Using Multiple Imputation for missing data, regression analysis showed significantly greater ORs for the SCBSCT-BA condition (vs. SCBSCT) at the end of treatment and at 3-months follow-up. At 6-, and 12-months follow-ups, ORs for the SCBSCT-BA condition, although greater, did not reach statistical significance. Multilevel analysis showed that abstinence was related to reductions in depressive symptoms. Conclusions: SCBSCT-BA obtained positive results at short and medium term. Participants who quit smoking experienced a significant reduction in depressive symptoms. Findings support the benefit of adding BA to a cognitive-behavioral smoking cessation treatment. Trial registration: . [ABSTRACT FROM AUTHOR]

In recent years there has been an increase in research on missing data problems, with multiple imputation being a fundamental alternative; where data sets often present complexities that are currently difficult to manage appropriately in the probability framework, but relatively simple to deal with imputation; For this reason, this article describes a series of practical aspects to apply this methodology in the case of carbon capture modeling for Colombia, based on the World Bank databases including missing data reaching R² of 79.2988%, highlighting that when estimating said data and recalculating the respective model, a greater R² is evidenced, being of 94.76901%, which evidences a substantial improvement of the respective multiple linear regression model as such. [ABSTRACT FROM AUTHOR]

Highlights • A new hybrid method for the imputation of missing values is proposed. • The method is based on a novel fuzzy c-means, mutual information, and regression. • Performance of imputation increases by using Grey in the fuzzy c-means algorithm. • The proposed method outperforms five existing imputation methods, in most cases. • The proposed method can also provide high classification accuracies. Abstract The presence of missing values in real-world data is not only a prevalent problem but also an inevitable one. Therefore, missing values should be handled carefully before the mining or learning process. This paper proposes a novel technique to impute missing data. It employs a new version of Fuzzy c-Means clustering algorithm which benefits from advantages of Grey Relational Grade over Minkowski-like similarity measures. To impute a missing value more accurately, it also performs a local mutual information based feature selection in each cluster to select only highly relevant features. Briefly, missing values are imputed in the following steps. First, the algorithm finds the importance of each missing attribute. Next, input instances are separated into several fuzzy clusters. Then, the algorithm selects clusters which satisfy a minimum condition. After that, it chooses highly dependent features of instances within each cluster using a mutual information based feature selection approach. When the features are selected, regression models will be applied to the selected features of the selected clusters to provide estimations for a missing value. Finally, the missing value is imputed through a weighted average of estimated values obtained from the previous step. Three well-known evaluation criteria and the accuracy of classification task are used to assess the performance of the proposed method. The experimental results for seven UCI data sets with different missing ratios and strategies indicate that the proposed algorithm outperforms five other imputation methods in general. [ABSTRACT FROM AUTHOR]