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Predavanje za predmet Tehnike obrade biomedicinskih signala na master akademskim studijama na Elektrotehničkom fakultetu Univerziteta u Beogradu.

Ο όρος ελλιπή δεδομένα (missing data) σημαίνει τις περιπτώσεις όπου για κάποια στοιχεία ενός δείγματος δεν έχουμε καταγράψει την τιμή μίας ή περισσοτέρων μεταβλητών (ιδιοτήτων). Παραδείγματα: Ένα ερωτηματολόγιο με αναπάντητες ερωτήσεις, μία βυζαντινή εικόνα που έχει καταστραφεί σε κάποια σημεία, η ελλιπής καταγραφή από ένα τηλεσκόπιο κ.λπ.. Η επεξεργασία ελλιπών δεδομένων οδηγεί σε επιστημονικά σφάλματα. Γι’ αυτό αναπτύχθηκαν μέθοδοι αποκατάστασής τους. Αυτές είναι από πολύ απλές (συμπλήρωση των δεδομένων που λείπουν από τον ερευνητή) μέχρι αρκετά σύνθετες. Κάτι τέτοιο συμβαίνει ιδιαιτέρως στην Αστρονομία, που (μαζί με την Κοσμολογία) οφείλει την ώθησή της στην αντιμετώπιση των ελλιπών δεδομένων. Λόγω του πρωταγωνιστικού ρόλου της Πληροφορικής, όλα τα παραπάνω είναι ψηφιακές εφαρμογές. Είναι βασικό ζήτημα η πρόληψη ελλιπών δεδομένων. Γι’ αυτό είναι σκόπιμο κάθε επιστήμονας να έχει κάποιες γνωσεις που θα τον βοηθήσουν στην κατάρτιση ενός κατάλληλου ερωτηματολογίου. Τα ελλιπή δεδομένα συναντώνται και στη Θεολογία. Είναι οι αιρέσεις. Αντιμετωπίζονται με Θεολογικές και Εκκλησιαστικές μεθόδους., Missing data are cases where for some of the elements of a sample, we can’t register the value of one or more variables (qualities). Some examples of missing data are a questionnaire with no answered questions, a byzantine icon destroyed in some points of its surface or a missing record of a telescope etc. The missing data processing will produce scientific errors. This is the reason that there are a lot of restoration methods. Some of them are quite simple (e.g. completion of missing values by the researcher) and some are very complicated. This often happens in Astronomy, one of the sciences which (together with Cosmology) have taken a boost of the treatment of missing data. Because of the great contribution of computers, all these are digital applications. It’s very significant to avoid the creation of missing data. For this reason, all scientints is useful to know some basic principles and to have helpful knowledge to create a suitable questionnaire. Missing data there are and in Theology, are the herecies. The methods here are Theologicals and Ecclesiasticals.

[EN] Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are defined over protected attributes, such as gender, race and nationality. Missing values are one frequent manifestation of all these latent causes: protected groups are more reluctant to give information that could be used against them, sensitive information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we present the first comprehensive analysis of the relation between missing values and algorithmic fairness for machine learning: (1) we analyse the sources of missing data and bias, mapping the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should discourage the consideration of missing values as the uncomfortable ugly data that different techniques and libraries for handling algorithmic bias get rid of at the first occasion, (3) we study the trade-off between performance and fairness when the rows with missing values are used (either because the technique deals with them directly or by imputation methods), and (4) we show that the sensitivity of six different machine-learning techniques to missing values is usually low, which reinforces the view that the rows with missing data contribute more to fairness through the other, nonmissing, attributes. We end the paper with a series of recommended procedures about what to do with missing data when aiming for fair decision making., Ministerio de Economia, Industria y Competitividad, Gobierno de Espana (ES), Grant/Award Number: RTI2018-094403-B-C3; Generalitat Valenciana, Grant/Award Number: PROMETEO/2019/09; Future of Life Institute, Grant/Award Number: RFP2-15; European Commission, Grant/Award Number: DG JRC - HUMAINT project

Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself. We describe a straightforward unsupervised imputation algorithm, a minimax approach based on optimal recovery, and derive probabilistic error bounds on downstream non-negative matrix factorization (NMF). Under certain geometric conditions, we prove upper bounds on NMF relative error, which is the first bound of this type for missing values. We also give probabilistic bounds for the same geometric assumptions. Experiments on image data and biological data show that this theoretically-grounded technique performs as well as or better than other imputation techniques that account for local structure. We also comment on imputation fairness.

We consider the problem of building the relationship of high-energy electron flux between Geostationary Earth Orbit (GEO) and Medium Earth Orbit (MEO). A time-series decomposition technique is first applied to the original data, resulting in trend and detrended part for both GEO and MEO data. Then we predict MEO trend with GEO data using three machine learning models: Linear Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). Experiment shows that RF gains best performance in all scenarios. Feature extraction analysis demonstrates that the inclusion of lagged features and (possible) ahead features is substantially helpful to the prediction. At last, an application of imputing missing values for MEO data is presented, in which RF model with selected features is used to handle the trend part while a moving block method is for the detrended part.

This paper is motivated by the fact that, although temperature readings made by Vaisala RS41 radiosondes at GRUAN sites (https://www.gruan.org/, last access: 30 November 2020) are given at 1 s resolution, for various reasons, missing data are spread along the atmospheric profile. Such a problem is quite common with radiosonde data and other profile data. Hence, (linear) interpolation is often used to fill the gaps in published data products. From this perspective, the present paper considers interpolation uncertainty, using a statistical approach to understand the consequences of substituting missing data with interpolated data. In particular, a general framework for the computation of interpolation uncertainty based on a Gaussian process (GP) set-up is developed. Using the GP characteristics, a simple formula for computing the linear interpolation standard error is given. Moreover, the GP interpolation is proposed as it provides an alternative interpolation method with its standard error. For the Vaisala RS41, the two approaches are shown to provide similar interpolation performances using an extensive cross-validation approach based on the block-bootstrap technique. Statistical results about interpolation uncertainty at various GRUAN sites and for various missing gap lengths are provided. Since both approaches result in an underestimation of the interpolation uncertainty, a bootstrap-based correction formula is proposed. Using the root mean square error, it is found that, for short gaps, with an average length of 5 s, the average uncertainty is less than 0.10 K. For larger gaps, it increases up to 0.35 K for an average gap length of 30 s and up to 0.58 K for a gap of 60 s. It is concluded that this approach could be implemented in a future version of the GRUAN data processing.

PURPOSE: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.DATA AND METHODS: Open data for this study was obtained from the BCSC Data Resource (http://breastscreening.cancer.gov/). We conducted two ablation-type experiments to compare the robustness of different classifiers where we randomly switched known information to missing with a missing probability of pm in one experiment, and randomly corrupted the existing information with a probability of pc in another experiment. We considered three prominent machine-learning classifiers such as Logistic regression (LR), Random Forests (RF) and a custom Neural Network (NN) architecture and compared their degradation of discrimination performance as a function of increasing probability of missing or inaccurate data.RESULTS: LR, RF and custom NN resulted in an Area Under Curve (AUC) of 0.645, 0.643 and 0.649, respectively, on a test set with 500,000 total observations. When we manipulated the data by varying probabilities pm and pc from 0 to 1, NN resulted in better performance in terms of AUC compared to RF and LR as long as less than half the data was missing/inaccurate (that is, for values of pm < 0.5 and pc < 0.5). However, for missing (pm) or corruption (pc) probabilities above 0.5, LR gave similar performance as the custom NN. RF resulted in overall poorer performance when the data had additional missing or incorrect entries.CONCLUSION: In cases where the input information is missing or inaccurate, our experiments show that the proposed custom NN provides reliable risk estimates in medical datasets like BCSC. These results are particularly important in health care applications where not every attribute of the individual participant might be available..

The Publisher's final version can be found by following the DOI link. Data quality plays a crucial role in the context of smart buildings. Meanwhile, missing data is relatively common in acquired datasets from sensors within the smart buildings. Poor data could result in a big bias in forecasting, control and operational services. Despite the common techniques to handle missing data, it is essential to systematically select the most appropriate approach for such missing values. This paper aims to focus on the lift systems as one of the essential parts in the smart buildings by exploring the most appropriate data imputation methods to handle missing data and to provide its service and allow a better understanding of patterns to issue the correct control actions based on forecasted models. The imputed data is not only investigated statistically but also modelled through machine learning algorithm to explore the impact of selecting inappropriate imputation techniques. Seven imputation techniques deployed on datasets with three level of missing values including 10%, 20% and 30% and the performance of methods examined through the normalized root mean square error (NRMSE) approach. In addition, the interaction between imputation techniques and a machine learning algorithm, namely random forest were examined. Findings from this paper can be employed in identifying an appropriate imputation technique not only within the lift datasets, but smart building context.

In this work, we used the MICE (Multivariate Imputation by Chained Equations) technique to impute missing daily data from six meteorological variables (precipitation, temperature, relative humidity, atmospheric pressure, wind speed and insolation) from 96 stations located in the northeast region of Brazil (NEB) for the period from 1961 to 2014. We then applied tests with a quality control system (QCS) developed for the detection, correction and possible replacement of suspicious data. Both the applied gap filling technique and the QCS showed that it was possible to solve two of the biggest problems found in time series of daily data measured in meteorological stations: the generation of plausible values for each variable of interest, in order to remedy the absence of observations, and how to detect and allow proper correction of suspicious values arising from observations.

We describe imputation strategies resistant to outliers, through modifications of the simple imputation method proposed by Krzanowski and assess their performance. The strategies use a robust singular value decomposition, do not depend on distributional or structural assumptions and have no restrictions as to the pattern or missing data mechanisms. They are tested through the simulation of contamination and unbalance, both in artificially generated matrices and in a matrix of real data from an experiment with genotype-by-environment interaction. Their performance is assessed by means of prediction errors, the squared cosine between matrices, and a quality coefficient of fit between imputations and true values. For small matrices, the best results are obtained by applying robust decomposition directly, while for larger matrices the highest quality is obtained by eliminating the singular values of the imputation equation.

Complete hydrological time series are necessary for water resources management and modeling. This can be challenging in data scarce environments where data gaps are ubiquitous. In many applications, repetitive gaps can have unfortunate consequences including ineffective model calibration, unreliable timing of peak flows, and biased statistics. Here, Direct Sampling (DS) is used as a non-parametric stochastic method for infilling gaps in daily streamflow records. A thorough gap-filling framework including the selection of predictor stations and the optimization of the DS parameters is developed and applied to data collected in the Volta River basin, West Africa. Various synthetic missing data scenarios are developed to assess the performance of the method, followed by a real-case application to the existing gaps in the flow records. The contribution of this study includes the assessment of the method for different climatic zones and hydrological regimes and for different upstream-downstream relations among the gauging stations used for gap filling. Tested in various missing data conditions, the method allows a precise and reliable simulation of the missing data by using the data patterns available in other stations as predictor variables. The developed gap-filling framework is transferable to other hydrological applications, and it is promising for environmental modeling.

Recovering missing values plays a significant role in time series tasks in practical applications. How to replace the missing data and build the dependency relations from the incomplete sample set is still a challenge. The previous research has found that residual network (ResNet) helps to form a deep network and cope with degradation problem by shortcut connection. Gated recurrent unit (GRU) can improve network model and reduce training parameters by update gate which takes the place of forgetting gate and output gate in long short-term memory (LSTM). Inspired by this finding, we observe that shortcut connection and mean of global revealed information can model the relationship among missing items, the previous and overall revealed information. Hence, we design an imputation network with decay factor for shortcut connection and mean of the global revealed information in GRU, called decay residual mean imputation GRU (DRMI-GRU). We introduce a decay residual mean unit (DRMU), which takes full advantage of the previous and global revealed information to model incomplete time series; and the decay factor is applied to balance the previous long-term dependencies and all non-missing values in the sample set. In addition, a mask unit is designed to check the missing data existing or not. An extensive body of empirical comparisons with other existing imputation algorithms over real-world data and public dataset with different ratio of missing data verifies the performance of our model.

Real-time underwater monitoring has been widely applied in many applications of underwater wireless sensor networks (UWSNs). Due to the long acoustic communication delays, the real-time data collection in UWSNs is challenging. Moreover, the underwater acoustic transmission faces the problem of high data loss rate, which causes a longer delay time due to the need for packet retransmissions. To address these problems, we propose a recurrent neural network (RNN)-based underwater monitoring framework with the consideration of delay, energy, and data quality. We drop the automatic retransmission mechanism applied in the MAC protocols to reduce the long end-to-end delay and energy cost. Facing high data loss, we propose an efficient RNN learning model, LSTM-Decay, to analyze the raw data with the time-related decay weights features and predict the missing values. The experiments with the real-world underwater sensing datasets show that our learning model can achieve an accurate estimation with different degrees of missing rates and can provide better performance compared with the non-RNN and RNN baselines.

It is recognized that the performance of any prediction model is a function of several factors. One of the most significant factors is the adopted preprocessing techniques. In other words, preprocessing is an essential process to generate an effective and efficient classification model. This paper investigates the impact of the most widely used preprocessing techniques, with respect to numerical features, on the performance of classification algorithms. The effect of combining various normalization techniques and handling missing values strategies is assessed on eighteen benchmark datasets using two well-known classification algorithms and adopting different performance evaluation metrics and statistical significance tests. According to the reported experimental results, the impact of the adopted preprocessing techniques varies from one classification algorithm to another. In addition, a statistically significant difference between the considered data preprocessing techniques is demonstrated.

International audience; The classification analysis of missing data is still a challenging task since the training patterns may be insufficient and incomplete in many fields. To train a high-performance classifier and pursue high accuracy, we learn a credal classifier based on an optimized and adaptive multi-estimation (OAME) method for missing data imputation on training and test sets. In OAME, some incomplete training patterns are estimated as multiple versions by a global optimization method thereby expanding the training set. On the other hand, the test pattern is adaptively estimated as one or multiple versions depending on the neighbors. For the test pattern with multiple versions, the corresponding outputs with different discounting factors (weights), represented by the basic belief assignments (BBAs), are fused for final credal classification based on evidence theory. The discounting factor contains two aspects: the importance and reliability factors that are used respectively to quantify the importance of the edited version itself and to represent the reliability of the classification result of the version. The effectiveness of OAME is widely validated on several real datasets and critically compared to other related methods.

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite&rsquo, s true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography-mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org. Notes The MS proteomics data have been deposited to the ProteomeXchange consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD019880 and PXD019881.

In practice, many medical domain datasets are incomplete, containing a proportion of incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem of incomplete datasets. To impute missing values, some of the observed data (i.e., complete data) are generally used as the reference or training set, and then the relevant statistical and machine learning techniques are employed to produce estimations to replace the missing values. Since the collected dataset usually contains a certain number of feature dimensions, it is useful to perform feature selection for better pattern recognition. Therefore, the aim of this paper is to examine the effect of performing feature selection on missing value imputation of medical datasets. Experiments are carried out on five different medical domain datasets containing various feature dimensions. In addition, three different types of feature selection methods and imputation techniques are employed for comparison. The results show that combining feature selection and imputation is a better choice for many medical datasets. However, the feature selection algorithm should be carefully chosen in order to produce the best result. Particularly, the genetic algorithm and information gain models are suitable for lower dimensional datasets, whereas the decision tree model is a better choice for higher dimensional datasets.

Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

Proceedings of: International Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015) Missing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed in the literature to face this problem. In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation. This method improves imputation performance by artificially deleting values in the input features and using them as targets in the training process. Nevertheless, although the deletion of samples is demonstrated to be really efficient, it may cause an imbalance between the distributions of the training and the test sets. In order to solve this issue, a compensation mechanism is proposed based on a slight modification of the error function to be optimized. Experiments over several datasets show that the deletion and compensation not only involve improvements in imputation but also in classification in comparison with other classical techniques. The work of A. R. Figueiras-Vidal has been partly supported by Grant Macro-ADOBE (TEC 2015-67719-P, MINECO/FEDER&FSE). The work of J.L. Sancho-Gómez has been partly supported by Grant AES 2017 (PI17/00771, MINECO/FEDER).

The data-dependent acquisition in mass spectrometry-based proteomics combined with quantitative analysis using isobaric labeling (iTRAQ and TMT) inevitably introduces missing values in proteomic experiments where a number of LC-runs are combined, especially in the growing field of shotgun clinical proteomics, where the protein profiles from the proteomics analysis of several hundred patient samples are compared and correlated to clinical traits such as a specific disease or disease treatment in order to link specific outcomes to one or more proteins. In the context of clinical research it is evident that missing values in such datasets reduce the power of the downstream statistical analysis therefore may hampers the linking of the expression of disease traits to the expression of specific proteins that may be useful for prognostic, diagnostic, or predictive purposes. In our study, we tested three data imputation approaches initially developed for microarray data for the imputation of missing values in datasets that are generated by several runs of shotgun proteomic experiments and where the data were relative protein abundances based on isobaric tags (iTRAQ and TMT). Our conclusion is that imputation methods based on k Nearest Neighbors successfully impute missing values in datasets with up to 50% missing values.

This paper is aimed at deriving some specific-oriented bootstrap confidence intervals for missing sequences of observations in multivariate time series. The procedure is based on a spatial-dynamic model and imputes the missing values using a linear combination of the neighbor contemporary observations and their lagged values. The resampling procedure implements a residual bootstrap approach which is then used to approximate the sampling distribution of the estimators of the missing values. The normal based and the percentile bootstrap confidence intervals have been computed. A Monte Carlo simulation study shows the good empirical coverage performance of the proposal, even in the case of long sequences of missing values.

Background In this study, the correlation between test motivation and performance on an economic knowledge test was investigated. To this end, the economic knowledge of 1018 students in vocational schools in Germany was assessed using a standardized test, and their self-reported test motivation and interest in receiving feedback on their performance on the test functioned as indicators of test motivation. The expectancy-value model served as the theoretical basis, while this paper followed Knekta and Eklöf (J Psychoeduc Assess 33(7):662–673, 2015) and focused on invested effort in particular. Further, the number of missing values on the test was examined as a potential external criterion for test motivation. The correlation between gender and test motivation being a subject of frequent discussion in the literature, gender was incorporated as a control variable into the modeling. Methods and results Three structural equation models of the results show that self-reported test motivation (direct indicator) and the number of missing values on the test (external criterion) correlated significantly with the economic knowledge test score achieved. Interest in receiving feedback (indirect indicator) had no significant correlation with the economic knowledge test score. However, there is a positive correlation between interest in receiving feedback and self-reported test motivation. The analyses taking into account gender show that there is no correlation between gender and interest in receiving feedback or gender and self-reported test motivation. There are, however, correlations between gender and the economic knowledge test score as well as between gender and the number of missing values. Conclusions The findings underline the importance of a differentiated view on the assessment of correlations between test motivation and test performance. Thereby, dividing indicators of test motivation into direct (self-reported test motivation) and indirect (interest in receiving feedback) indicators, as well as taking into account the external criterion (number of missing values), in particular, were seen as the value added by this study.

In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled &delta, 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

KarsTS 2.2 is free, open-source, R-based software for microclimate time series, especially suited to the study of underground or highly insulated environments. The time series of interest include air temperature, humidity, and CO2 and 222Rn content, amongst others. These time series usually pose problems such as gaps, outliers, noise or relative shortness. KarsTS was born as a package for gap filling and thus, it offers multiple univariate and multivariate gap-filling tools well suited to these variables. However, as KarsTS was intended to be a self-sufficient program, it soon grew to encompass several tools for linear and nonlinear time series analysis, preprocessing and plotting. Indeed, many of these variables show a nonlinear behavior that is often disregarded; for this reason, we aim to spread and facilitate the use of some methodologically appropriate analysis tools, even amongst researcher that do not feel comfortable using a console. In this paper, we introduce an overview of KarsTS functionality and we show its potential through some practical application examples on four-year time series of temperature from the Rull cave (Spain). This research was funded by the Spanish Ministry of Economy and Competitiveness Projects [CGL2011-25162, CGL2016-78318-C2-1-R, CGL2016-78318-C2-2-R and RTI2018-099052-B-I00]. A post-doctoral research fellowship was awarded to S. Cuezva by the University of Almería (Hipatia Programme).

Background With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses. Results We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under the curve, bias and MSE for parameter estimates), our simulation results indicate that BayesMetab outperformed other imputation algorithms when there is a mixture of missingness due to MAR and MNAR. Further, our approach was competitive with other methods tailored specifically to MNAR in situations where missing data were completely MNAR. Applying our approach to an analysis of metabolomics data from a mouse myocardial infarction revealed several statistically significant metabolites not previously identified that were of direct biological relevance to the study. Conclusions Our findings demonstrate that BayesMetab has improved performance in imputing the missing values and performing statistical inference compared to other current methods when missing values are due to a mixture of MNAR and MAR. Analysis of real metabolomics data strongly suggests this mixture is likely to occur in practice, and thus, it is important to consider an imputation model that accounts for a mixture of missing data types.

High density air monitoring campaigns provide spatial patterns of pollutant concentrations which are integral in exposure assessment. Such analysis can assist with the determination of links between air quality and health outcomes, however, problems due to missing data can threaten to compromise these studies. This research evaluates four methods; mean value imputation, inverse distance weighting (IDW), inter-species ratios, and regression, to address missing spatial concentration data ranging from one missing data point up to 50% missing data. BTEX (benzene, toluene, ethylbenzene, and xylenes) concentrations were measured in Windsor and Sarnia, Ontario in the fall of 2005. Concentrations and inter-species ratios were generally similar between the two cities. Benzene (B) was observed to be higher in Sarnia, whereas toluene (T) and the T/B ratios were higher in Windsor. Using these urban, industrialized cities as case studies, this research demonstrates that using inter-species ratios or regression of the data for which there is complete information, along with one measured concentration (i.e. benzene) to predict for missing concentrations (i.e. TEX) results in good agreement between predicted and measured values. In both cities, the general trend remains that best agreement is observed for the leave-one-out scenario, followed by 10% and 25% missing, and the least agreement for the 50% missing cases. In the absence of any known concentrations IDW can provide reasonable agreement between observed and estimated concentrations for the BTEX species, and was superior over mean value imputation which was not able to preserve the spatial trend. The proposed methods can be used to fill in missing data, while preserving the general characteristics and rank order of the data which are sufficient for epidemiologic studies.

Pairwise comparison matrix as a crucial component of AHP, presents the prefer- ence relations among alternatives. However, in many cases, the pairwise comparison matrix is difficult to complete, which obstructs the subsequent operations of the clas- sical AHP. In this paper, based on DEMATEL which has ability to derive the total relation matrix from direct relation matrix, a new completion method for incomplete pairwise comparison matrix is proposed. The proposed method provides a new per- spective to estimate the missing values with explicit physical meaning. Besides, the proposed method has low computational cost. This promising method has a wide application in multi-criteria decision-making., Comment: 29 pages

BACKGROUND: The number of contingent valuation (CV) studies in dental medicine using willingness-to-pay (WTP) methodology has substantially increased in recent years. Missing values due to absent information (i.e., missingness) or false information (i.e., protest zeros) are a common problem in WTP studies. The objective of this study is to evaluate the prevalence of missing values in CV studies in dental medicine, to assess how these have been dealt with, and to suggest recommendations for future research. METHODS: We systematically searched electronic databases (MEDLINE, Web of Science, Cochrane Library, PROSPERO) on 8 June 2021, and hand-searched references of selected reviews. CV studies in clinical dentistry using WTP for valuing a good or service were included. RESULTS: We included 49 WTP studies in our review. Out of these, 19 (38.8%) reported missing values due to absent information, and 28 (57.1%) reported zero values (i.e., WTP valued at zero). Zero values were further classified into true zeros (i.e., representing the underlying preference of the respondent) or protest zeros (i.e., false information as a protest behavior) in only 9 studies. Most studies used a complete case analysis to address missingness while only one study used multiple imputation. CONCLUSIONS: There is uncertainty in the dental literature on how to address missing values and zero values in CV studies. Zero values need to be classified as true zeros versus protest zeros with follow-up questions after the WTP elicitation procedure, and then need to be handled differently. Advanced statistical methods are available to address both missing values due to missingness and due to protest zeros but these are currently underused in dental medicine. Failing to appropriately address missing values in CV studies may lead to biased WTP estimates of dental interventions. ispartof: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH vol:18 issue:14 ispartof: location:Switzerland status: published

In this study, it is aimed to investigate the impact of different missing data handling methods on the detection of Differential Item Functioning methods (Mantel Haenszel and Standardization methods based on Classical Test Theory and Likelihood Ratio Test method based on Item Response Theory). In this regard, on the data acquired from 1046 candidates who entered to Foreign National Student Exam (FNSE) held in year 2016 by Mersin University (MEU) and answered Basic Skills subtest, using different missing data handling methods, differential item functioning analyses with Mantel Haenszel, Standardization and Likelihood Ratio Test methods are performed. Basic Skills test consists of 80 multiple choice items. The items are all binary scored (1-0) items. Among the participants 523 are female and 523 are male. The findings showed that the number of items flagged as DIF has changed with the used missing data handling methods. The DIF detection methods based on Classical Test Theory are more consistent within themselves compared to DIF detection method based on Item Response Theory, whereas the used missing data handling methods differentiate the DIF detected items and this difference reaches a significant level for Mantel Haenszel method

A decision support system with data-driven methods is of great significance for the prognosis of scoliosis. However, developing an accurate and interpretable data-driven decision support system is challenging: 1) the scoliosis data collected from clinical environments is heterogeneous, unstructured, and incomplete; 2) the cause of adolescent idiopathic scoliosis is still unknown, and the effects of some measured indicators are not clear; and 3) some treatments like wearing a brace will affect the progression of scoliosis. The main contributions of the paper include: 1) propose and incorporate different imputation methods like Local Linear Interpolation (LLI) and Global Statistic Approximation (GSA) to deal with complicated types of incomplete data in clinical environments; 2) identify important features that are relevant to the severity of scoliosis with embedded method; and 3) establish and compare the scoliosis prediction models with multiple linear regression, ${k}$ nearest neighbor, tree, support vector machine, and random forest algorithms. The prediction performance is evaluated in terms of mean absolute error, root mean square error, mean absolute percentage error, and the Pearson correlation coefficient. With only a few critical features, the prediction models can achieve satisfactory performance. Experiments show that the models are highly interpretable and viable to support the decision-making in clinical environments.

A recent paper published time series of concentrations of chemicals in drinking water collected from the bottom of Lake Mead, a major American water supply reservoir. Data were compared to water level using only linear regression. This creates an opportunity for students to analyze these data further. This article presents a structured introduction to time series decomposition that compares long-term and seasonal components of a time series of a single chemical (meprobamate) with those of two supporting datasets (reservoir volume and specific conductance). For the chemical data, this must be preceded by estimation of missing datum points. Results show that linear regression analyses of time series data obscure meaningful detail and that specific conductance is the important predictor of seasonal chemical variations. To learn this, students must execute a linear regression, estimate missing data using local regression, decompose time series, and compare time series using cross-correlation. Complete R code for these exercises appears in the supplementary information. This article uses real data and requires that students make and justify key decisions about the analysis. It can be a guided or an individual project. It is scalable to instructor needs and student interests in ways that are identified clearly in this article.

Missing values in data are common in real world applications. There are several methods that deal with this problem. In this paper we present lookahead selective sampling (LSS) algorithms for datasets with missing values. We developed two versions of selective sampling. The first one integrates a distance function that can measure the similarity between pairs of incomplete points within the framework of the LSS algorithm. The second algorithm uses ensemble clustering in order to represent the data in a cluster matrix without missing values and then run the LSS algorithm based on the ensemble clustering instance space (LSS-EC). To construct the cluster matrix, we use the k-means and mean shift clustering algorithms especially modified to deal with incomplete datasets. We tested our algorithms on six standard numerical datasets from different fields. On these datasets we simulated missing values and compared the performance of the LSS and LSS-EC algorithms for incomplete data to two other basic methods. Our experiments show that the suggested selective sampling algorithms outperform the other methods.

Sustainability scholars frame urbanization as a multidimensional concept with divergent environmental impacts. Through synthesizing recent quantitative studies of urbanization in criminology, we evaluated this multidimensional framework in a longitudinal, cross-national analysis of homicide rates for 217 countries between 2000 and 2015. For the analysis, we also highlighted the issue of missing data, a common concern for cross-national scholars in a variety of disciplines. While controlling for other relevant factors, we compared results from panel models that use the common technique of listwise deletion (n = 113) and from structural equation models (SEM) that handle missing values with full information maximum likelihood (n = 216). While the estimates for the control variables are non-significant in the SEM approach, the findings for the urbanization variables were robust and multidimensional. In particular, while the proportion of the population that is urban is positively related to homicide, the proportion of the population living in large cities of at least one million inhabitants is significantly and negatively related to homicide in all models. Given our focus on urbanization, we outline our contribution not only in the context of criminology but also the cross-national sustainability literature, which often uses similar variables with missing values.

Multivariate time series are often accompanied with missing values, especially in clinical time series, which usually contain more than 80% of missing data, and the missing rates between different variables vary widely. However, few studies address these missing rate differences and extract univariate missing patterns simultaneously before mixing them in the model training procedure. In this paper, we propose a novel recurrent neural network called variable sensitive GRU (VS-GRU), which utilizes the different missing rate of each variable as another input and learns the feature of different variables separately, reducing the harmful impact of variables with high missing rates. Experiments show that VS-GRU outperforms the state-of-the-art method in two real-world clinical datasets (MIMIC-III, PhysioNet).

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination [inline-formula]. On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.

© 2019 Association for Computing Machinery. The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

Predicting the progression of Alzheimer’s Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the development of novel machine learning algorithms. This study proposes a novel machine learning algorithm for predicting the progression of Alzheimer’s disease using a distributed multimodal, multitask learning method. More specifically, each individual task is defined as a regression model, which predicts cognitive scores at a single time point. Since the prediction tasks for multiple intervals are related to each other in chronological order, multitask regression models have been developed to track the relationship between subsequent tasks. Furthermore, since subjects have various combinations of recording modalities together with other genetic, neuropsychological and demographic risk factors, special attention is given to the fact that each modality may experience a specific sparsity pattern. The model is hence generalized by exploiting multiple individual multitask regression coefficient matrices for each modality. The outcome for each independent modality-specific learner is then integrated with complementary information, known as risk factor parameters, revealing the most prevalent trends of the multimodal data. This new feature space is then used as input to the gradient boosting kernel in search for a more accurate prediction. This proposed model not only captures the complex relationships between the different feature representations, but it also ignores any unrelated information which might skew the regression coefficients. Comparative assessments are made between the performance of the proposed method with several other well-established methods using different multimodal platforms. The results indicate that by capturing the interrelatedness between the different modalities and extracting only relevant information in the data, even in an incomplete longitudinal dataset, will yield minimized prediction errors.