Abstract

Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of Imputation Uncertainty.PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.Theory of Inference Based on the Likelihood Function.Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism.Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse.Large-Sample Inference Based on Maximum Likelihood Estimates.Bayes and Multiple Imputation.PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS.Multivariate Normal Examples, Ignoring the Missing-Data Mechanism.Models for Robust Estimation.Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism.Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism.Nonignorable Missing-Data Models.References.Author Index.Subject Index.

Keywords

Missing dataImputation (statistics)Computer scienceInferenceLikelihood functionStatisticsData miningEconometricsMaximum likelihoodMathematicsArtificial intelligence

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Publication Info

Year
1988
Type
article
Volume
30
Issue
4
Pages
455-455
Citations
3118
Access
Closed

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Subir Ghosh, Roderick J. A. Little, Donald B. Rubin (1988). Statistical Analysis with Missing Data. Technometrics , 30 (4) , 455-455. https://doi.org/10.2307/1269814

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DOI
10.2307/1269814