Statistical Methods for Handling Incomplete Data  book cover
1st Edition

Statistical Methods for Handling Incomplete Data

ISBN 9781439849637
Published July 23, 2013 by Chapman and Hall/CRC
223 Pages 2 B/W Illustrations

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Book Description

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Suitable for graduate students and researchers in statistics, the book presents thorough treatments of:

  • Statistical theories of likelihood-based inference with missing data
  • Computational techniques and theories on imputation
  • Methods involving propensity score weighting, nonignorable missing data, longitudinal missing data, survey sampling, and statistical matching

Assuming prior experience with statistical theory and linear models, the text uses the frequentist framework with less emphasis on Bayesian methods and nonparametric methods. It includes many examples to help readers understand the methodologies. Some of the research ideas introduced can be developed further for specific applications.

Table of Contents

How to Use This Book

Likelihood-Based Approach
Observed Likelihood
Mean Score Approach
Observed Information

Factoring Likelihood Approach
EM Algorithm
Monte Carlo Computation
Monte Carlo EM
Data Augmentation

Basic Theory for Imputation
Variance Estimation after Imputation
Replication Variance Estimation
Multiple Imputation
Fractional Imputation

Propensity Scoring Approach
Regression Weighting Method
Propensity Score Method
Optimal Estimation
Doubly Robust Method
Empirical Likelihood Method
Nonparametric Method

Nonignorable Missing Data
Nonresponse Instrument
Conditional Likelihood Approach
Generalized Method of Moments (GMM) Approach
Pseudo Likelihood Approach
Exponential Tilting (ET) Model
Latent Variable Approach
Capture–Recapture (CR) Experiment

Longitudinal and Clustered Data
Ignorable Missing Data
Nonignorable Monotone Missing Data
Past-Value-Dependent Missing Data
Random-Effect-Dependent Missing Data

Application to Survey Sampling
Calibration Estimation
Propensity Score Weighting Method
Fractional Imputation
Fractional Hot Deck Imputation
Imputation for Two-Phase Sampling
Synthetic Imputation

Statistical Matching
Instrumental Variable Approach
Measurement Error Models
Causal Inference



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"… this book nicely blends the theoretical material and its application through examples, and will be of interest to students and researchers as a textbook or a reference book. Extensive coverage of recent advances in handling missing data provides resources and guidelines for researchers and practitioners in implementing the methods in new settings. … I plan to use this as a textbook for my teaching and highly recommend it."
Biometrics, September 2014