Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
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TLDR
2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI) are presented and may eventually extend the ML and MI methods that currently represent the state of the art.Abstract:
Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.read more
Citations
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TL;DR: In this article, the authors present a detailed, worked-through example drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology.
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Missing data analysis: making it work in the real world.
TL;DR: This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods, and strategies for reducing attrition bias.
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Link prediction in complex networks: A survey
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How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory
TL;DR: It is recommended that researchers using MI should perform many more imputations than previously considered sufficient, based on γ, and take into consideration one’s tolerance for a preventable power falloff due to using too few imputations.
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TL;DR: The problem of missing data concepts of MCAR, MAR and MNAR simple solutions that do not (always) work multiple imputation in a nutshell and some dangers, some do's and some don'ts are covered.
References
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Multiple Imputation for Missing Data: Concepts and New Development
TL;DR: This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques, and presents new SAS R procedures for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets.
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Ignorability and Coarse Data
TL;DR: In this article, the authors present a general statistical model for data coarsening, which includes as special cases rounded, heaped, censored, partially categorized and missing data, and establish simple conditions under which the possible stochastic nature of the coarsing mechanism can be ignored when drawing Bayesian and likelihood inferences and thus the data can be validly treated as grouped data.
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Maximum Likelihood Estimates for a Multivariate Normal Distribution when Some Observations are Missing
TL;DR: In this paper, the authors give an approach to derive maximum likelihood estimates of parameters of multivariate normal distributions in cases where some observations are missing (Edgett [2] and Lord [3], [4]).
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Multiple imputation in practice: comparison of software packages for regression models with missing variables
TL;DR: A number of software packages that implement multiple imputation, originally proposed by Rubin in a public use dataset setting, are described and evaluated, and the interface, features, and results are compared.
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