Statistical Analysis with Missing Data, 2nd Edition
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- Book aims to survey current methodology for handling missing-data problems
- Presents a likelihood-based theory for analysis with missing data that systematizes the methods and provides a basis for future advances
- Part I discusses historical appraches to missing-value problems
- Part II presents a systematic apprach to the analysis of data with missing valuees, where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing data mechanisms
- Part III presents applications of hte approach in a variety of contexts including regressoin; factor analysis; contingency table analysis; time series; and sample survey inference
- Briefly reviews basic principles of inferences based on likelihoods, expecting readers to be familiar with these concepts
- Some chapters assume familiarity with analysis of variance for experimental designs; survey sampling; loglinear models for contingency tables
- Specific examples introduce factor analysis, time series, etc.
- Discussion of examples is self-contained and does not require specialized knowledge