Format:
xv, 381 S.
,
graph. Darst.
Edition:
2nd ed.
ISBN:
0471183865
Series Statement:
Wiley series in probability and statistics
Content:
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.
Note:
MAB0014.001: M 06.0246
