Missing Data Analysis in Practice

ISBN: 9781482211924 出版年：2015 页码：227 Raghunathan, Trivellore CRC Press

Basic Concepts Introduction Definition of Missing Values Missing Data Pattern Missing Data Mechanism Problems with Complete-Case Analysis Analysis Approaches Basic Statistical Concepts A Chuckle or Two Weighting Methods Motivation Adjustment Cell Method Response Propensity Model Example Impact of Weights on Population Mean Estimates Post-Stratification Survey Weights Alternative to Weighted Analysis Inverse Probability Weighting Imputation Generation of Plausible Values Hot Deck Imputation Model Based Imputation Example Sequential Regression Imputation Multiple Imputation Introduction Basic Combining Rule Multivariate Hypothesis Testing Combining Test Statistics Basic Theory of Multiple Imputation Extended Combining Rules Some Practical Issues Revisiting Examples Example: St. Louis Risk Research Project Regression Analysis General Observations Revisiting St. Louis Risk Research Example Analysis of Variance Survival Analysis Example Longitudinal Analysis with Missing Values Introduction Imputation Model Assumption Example Practical Issues Weighting Methods Binary Example Nonignorable Missing Data Mechanisms Modeling Framework EM-Algorithm Inference under Selection Model Inference under Mixture Model Example Practical Considerations Other Applications Measurement Error Combining Information from Multiple Data Sources Bayesian Inference from Finite Population Causal Inference Disclosure Limitation Other Topics Uncongeniality and Multiple Imputation Multiple Imputation for Complex Surveys Missing Values by Design Replication Method for Variance Estimation Final Thoughts Bibliography Index Bibliographic Notes and Exercises appear at the end of each chapter.