Missing data: Our view of the state of the art.
Joseph L. Schafer,John W. Graham +1 more
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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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Book
Confirmatory Factor Analysis for Applied Research
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
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
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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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Flexible Imputation of Missing Data
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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Book ChapterDOI
On the Problem of the Most Efficient Tests of Statistical Hypotheses
Jerzy Neyman,Egon S. Pearson +1 more
TL;DR: The problem of testing statistical hypotheses is an old one as discussed by the authors and its origins are usually connected with the name of Thomas Bayes, who gave the well-known theorem on the probabilities a posteriori of the possible causes of a given event.
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A Satorra,PM Bentler +1 more
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Estimation of Regression Coefficients When Some Regressors are not Always Observed
TL;DR: In this paper, a new class of semiparametric estimators, based on inverse probability weighted estimating equations, were proposed for parameter vector α 0 of the conditional mean model when the data are missing at random in the sense of Rubin and the missingness probabilities are either known or can be parametrically modeled.
Journal ArticleDOI
A comparison of inclusive and restrictive strategies in modern missing data procedures.
TL;DR: A simulation was presented to assess the potential costs and benefits of a restrictive strategy, which makes minimal use of auxiliary variables, versus an inclusive strategy,Which shows that the inclusive strategy is to be greatly preferred.
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