Imputation of missing values is a strategy for handling non-responses in surveys or data loss in measurement processes, which may be more effective than ignoring them. When the variable represents a count, the literature dealing with this issue is scarce. Likewise, if problems of over- or under-dispersion are observed, generalisations of the Poisson distribution are recommended for carrying out imputation. In order to assess the performance of various regression models in the imputation of a discrete variable compared to classical counting models, this work presents a comprehensive simulation study considering a variety of scenarios and real data. To do so we compared the results of estimations using only complete data, and using imputations based on the Poisson, negative binomial, Hermite, and COMPoisson distributions, and the ZIP and ZINB models for excesses of zeros. The results of this work reveal that the COMPoisson distribution provides in general better results in any dispersion scenario, especially when the amount of missing information is large. When the variable presenting missing values is a count, the most widely used method is to assume that a classical Poisson model is the best alternative to impute the missing counts; however, in real-life research this assumption is not always correct, and it is common to find count variables exhibiting overdispersion or underdispersion, for which the Poisson model is no longer the best to use in imputation. In several of the scenarios considered the performance of the methods analysed differs, something which indicates that it is important to analyse dispersion and the possible presence of excess zeros before deciding on the imputation method to use. The COMPoisson model performs well as it is flexible regarding the handling of counts with characteristics of over- and under-dispersion, as well as with equidispersion.
Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GA…
We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which are standard assumptions of classic missing data imputation methods. Our methodology provides a simple solution that offers compatible imputation guarantees while relaxing assumptions for mis…
Missing data imputation for noisy time-series data and applications in healthcare
Faculty of Mathematics and Computer Science, University of Science（胡志明市科学大学数学与计算机科学学院）；Vietnam National University（越南国家大学）；University of Medicine and Pharmacy at Ho Chi Minh City（胡志明市医药大学）；Simula Metropolitan（西穆拉都会研究院）；AISIA Research Laboratory（AISIA研究院）
Machine learning iterative imputation methods have been well accepted by researchers for imputing missing data, but they can be time-consuming when handling large datasets. To overcome this drawback, parallel computing strategies have been proposed but their impact on imputation results and subsequent statistical analyses are relatively unknown. This study examines the two parallel strategies (variable-wise distributed computation and model-wise distributed computation) implemented in the random-forest imputation method, missForest. Results from the simulation experiments showed that the two p…
Imputation of missing data is a common application in various classification problems where the feature training matrix has missingness. A widely used solution to this imputation problem is based on the lazy learning technique, $k$-nearest neighbor (kNN) approach. However, most of the previous work on missing data does not take into account the presence of the class label in the classification problem. Also, existing kNN imputation methods use variants of Minkowski distance as a measure of distance, which does not work well with heterogeneous data. In this paper, we propose a novel iterative k…
Iterative imputation is a popular tool to accommodate missing data. While it is widely accepted that valid inferences can be obtained with this technique, these inferences all rely on algorithmic convergence. There is no consensus on how to evaluate the convergence properties of the method. Our study provides insight into identifying non-convergence in iterative imputation algorithms. We found that--in the cases considered--inferential validity was achieved after five to ten iterations, much earlier than indicated by diagnostic methods. We conclude that it never hurts to iterate longer, but su…
Multiple imputation (MI) is an established technique to handle missing data in observational studies. Joint modeling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel clustered data. However, MI approaches for ordinal clustered outcome variables have not been well studied, especially when there is informative cluster size (ICS). The purpose of this study is to describe different imputation and analysis strategies for the multilevel ordinal outcome when ICS exists. We conducted comprehensive Monte Carlo simulation studies to compare five different …
Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random (MAR) assumption -- a natural benchmark for monotone missingness -- does not model realistic beliefs about non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random (MNAR) mechanisms. The "no self-censoring" (NSC) model is such a mechanism and assumes the probability an outco…
Missing data on response variables are common in clinical studies. Corresponding to the uncertainty of missing mechanism, theoretical frameworks on controlled imputation have been developed. In practice, it is recommended to conduct a statistically valid analysis under the primary assumptions on missing data, followed by sensitivity analysis under alternative assumptions to assess the robustness of results. Due to the availability of software, controlled multiple imputation (MI) procedures, including delta-based and reference-based approaches, have become popular for analyzing continuous varia…
Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic complications of genomic data present significant challenges in developing an unbiased and informative procedure of phenotype-genotype association analysis by a statistical variable selection approach. In this paper we develop a coherent procedure of categorical phenotype-genotype association analysis, in the presence of missing values with non-ignorable missingn…
École de Technologie Supérieure, Université du Québec（魁北克大学高等技术学院）
Data acquisition and recording in the form of databases are routine operations. The process of collecting data, however, may experience irregularities, resulting in databases with missing data. Missing entries might alter analysis efficiency and, consequently, the associated decision-making process. This paper focuses on databases collecting information related to the natural environment. Given the broad spectrum of recorded activities, these databases typically are of mixed nature. It is therefore relevant to evaluate the performance of missing data processing methods considering this charact…
Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors that is not always available in daily practice. We describe two methods for real-time handling of missing predictor values when using prediction models in practice. We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implem…
Missing covariates in regression or classification problems can prohibit the direct use of advanced tools for further analysis. Recent research has realized an increasing trend towards the usage of modern Machine Learning algorithms for imputation. It originates from their capability of showing favourable prediction accuracy in different learning problems. In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction a…
University of Missouri（密苏里大学）；Meta Platforms, Inc.（元平台公司）；Harvard Medical School（哈佛医学院）；Duke University（杜克大学）
Multiple imputation (MI) is a popular approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is one of the most widely used MI algorithms for multivariate data, but it lacks theoretical foundation and is computationally intensive. Recently, missing data imputation methods based on deep learning models have been developed with encouraging results in small studies. However, there has been limited research on evaluating their performance in realistic settings compared to MICE, particularly in big surveys. We conduct exte…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Deep Learning (DL) methods have dramatically increased in popularity in recent years. While its initial success was demonstrated in the classification and manipulation of image data, there has been significant growth in the application of DL methods to problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of Variational Autoencoders (VAEs), a popular unsupervised DL architecture commonly utilized for dimension red…
In this paper, we expand the methodology presented in Mertens et. al (2020, Biometrical Journal) to the study of life-time (survival) outcome which is subject to censoring and when imputation is used to account for missing values. We consider the problem where missing values can occur in both the calibration data as well as newly - to-be-predicted - observations (validation). We focus on the Cox model. Methods are described to combine imputation with predictive calibration in survival modeling subject to censoring. Application to cross-validation is discussed. We demonstrate how conclusions br…
