Statistical Methods for Handling Incomplete Data (Hardback) book cover

Statistical Methods for Handling Incomplete Data

By Jae Kwang Kim, Jun Shao

© 2013 – Chapman and Hall/CRC

223 pages | 2 B/W Illus.

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Hardback: 9781439849637
pub: 2013-07-23
eBook (VitalSource) : 9781439849644
pub: 2013-07-16
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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.


"… 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

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



Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General