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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.

**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**

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.

**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**

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.

**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**

**Statistical Methods for Handling Incomplete Data** covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

- Statistical theories of likelihood-based inference with missing data
- Computational techniques and theories on imputation

**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**

**Statistical Methods for Handling Incomplete Data** covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

- Statistical theories of likelihood-based inference with missing data
- Computational techniques and theories on imputation

**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**

**Statistical Methods for Handling Incomplete Data** covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

- Statistical theories of likelihood-based inference with missing data
- Computational techniques and theories on imputation

**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**