Statistical methods for handling incomplete data /
Kim, Jae Kwang, 1968-
Statistical methods for handling incomplete data / Jae Kwang Kim, Department of Statistics Iowa State University, USA, Jun Shao, Department of Statistics University of Wisconsin - Madison, USA. - xi, 211 pages ; 25 cm
Includes bibliographical references (pages 201-207) and index.
Introduction
Introduction
Outline
How to Use This Book
Likelihood-Based Approach
Introduction
Observed Likelihood
Mean Score Approach
Observed Information
Computation
Introduction
Factoring Likelihood Approach
EM Algorithm
Monte Carlo Computation
Monte Carlo EM
Data Augmentation
Imputation
Introduction
Basic Theory for Imputation
Variance Estimation after Imputation
Replication Variance Estimation
Multiple Imputation
Fractional Imputation
Propensity Scoring Approach
Introduction
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
Callbacks
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
Introduction
Calibration Estimation
Propensity Score Weighting Method
Fractional Imputation
Fractional Hot Deck Imputation
Imputation for Two-Phase Sampling
Synthetic Imputation
Statistical Matching
Introduction
Instrumental Variable Approach
Measurement Error Models
Causal Inference
Bibliography
Index
"With the advances in statistical computing, there has been a rapid development of techniques and applications in missing data analysis. This book aims to cover the most up-to-date statistical theories and computational methods for analyzing incomplete data through (1)vigorous treatment of statistical theories on likelihood-based inference with missing data, (2) comprehensive treatment of computational techniques and theories on imputation, and (3) most up-to-date treatment of methodologies involving propensity score weighting, nonignorable missing, longitudinal missing, survey sampling application, and statistical matching. The book is suitable for use as a textbook for a graduate course in statistics departments or as a reference book for those interested in this area. Some of the research ideas introduced in the book can be developed further for specific applications"--
9781439849637 (hardback : acidfree paper)
2013010114
Missing observations (Statistics)
Multiple imputation (Statistics)
MATHEMATICS / Probability & Statistics / General.
QA276.8 / .K55 2014
519.54 KIM S
Statistical methods for handling incomplete data / Jae Kwang Kim, Department of Statistics Iowa State University, USA, Jun Shao, Department of Statistics University of Wisconsin - Madison, USA. - xi, 211 pages ; 25 cm
Includes bibliographical references (pages 201-207) and index.
Introduction
Introduction
Outline
How to Use This Book
Likelihood-Based Approach
Introduction
Observed Likelihood
Mean Score Approach
Observed Information
Computation
Introduction
Factoring Likelihood Approach
EM Algorithm
Monte Carlo Computation
Monte Carlo EM
Data Augmentation
Imputation
Introduction
Basic Theory for Imputation
Variance Estimation after Imputation
Replication Variance Estimation
Multiple Imputation
Fractional Imputation
Propensity Scoring Approach
Introduction
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
Callbacks
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
Introduction
Calibration Estimation
Propensity Score Weighting Method
Fractional Imputation
Fractional Hot Deck Imputation
Imputation for Two-Phase Sampling
Synthetic Imputation
Statistical Matching
Introduction
Instrumental Variable Approach
Measurement Error Models
Causal Inference
Bibliography
Index
"With the advances in statistical computing, there has been a rapid development of techniques and applications in missing data analysis. This book aims to cover the most up-to-date statistical theories and computational methods for analyzing incomplete data through (1)vigorous treatment of statistical theories on likelihood-based inference with missing data, (2) comprehensive treatment of computational techniques and theories on imputation, and (3) most up-to-date treatment of methodologies involving propensity score weighting, nonignorable missing, longitudinal missing, survey sampling application, and statistical matching. The book is suitable for use as a textbook for a graduate course in statistics departments or as a reference book for those interested in this area. Some of the research ideas introduced in the book can be developed further for specific applications"--
9781439849637 (hardback : acidfree paper)
2013010114
Missing observations (Statistics)
Multiple imputation (Statistics)
MATHEMATICS / Probability & Statistics / General.
QA276.8 / .K55 2014
519.54 KIM S