Statistical Analysis with Missing Data

ISBN-10: 0471802549

ISBN-13: 9780471802549

Edition: 9th 1987

List price: $110.00
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Book details

List price: $110.00
Edition: 9th
Copyright year: 1987
Publisher: John Wiley & Sons, Incorporated
Publication date: 5/11/1987
Binding: Hardcover
Pages: 304
Size: 6.32" wide x 9.53" long x 0.86" tall
Weight: 1.276
Language: English

Overview and Basic Approaches
The Problem of Missing Data
Missing-Data Patterns
Mechanisms That Lead to Missing Data
A Taxonomy of Missing-Data Methods
Missing Data in Experiments
The Exact Least Squares Solution with Complete Data
The Correct Least Squares Analysis with Missing Data
Filling in Least Squares Estimates
Bartlett's ANCOVA Method
Least Squares Estimates of Missing Values by ANCOVA Using Only Complete-Data Methods
Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares
Correct Least Squares Sums of Squares with More Than One Degree of Freedom
Complete-Case and Available-Case Analysis, Including Weighting Methods
Complete-Case Analysis
Weighted Complete-Case Analysis
Available-Case Analysis
Single Imputation Methods
Imputing Means from a Predictive Distribution
Imputing Draws from a Predictive Distribution
Estimation of Imputation Uncertainty
Imputation Methods that Provide Valid Standard Errors from a Single Filled-in Data Set
Standard Errors for Imputed Data by Resampling
Introduction to Multiple Imputation
Comparison of Resampling Methods and Multiple Imputation
Likelihood-Based Approaches to the Analysis of Missing Data
Theory of Inference Based on the Likelihood Function
Review of Likelihood-Based Estimation for Complete Data
Likelihood-Based Inference with Incomplete Data
A Generally Flawed Alternative to Maximum Likelihood: Maximizing Over the Parameters and the Missing Data
Likelihood Theory for Coarsened Data
Factored Likelihood Methods, Ignoring the Missing-Data Mechanism
Bivariate Normal Data with One Variable Subject to Nonresponse: ML Estimation
Bivariate Normal Monotone Data: Small-Sample Inference
Monotone Data With More Than Two Variables
Factorizations for Special Nonmonotone Patterns
Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse
Alternative Computational Strategies
Introduction to the EM Algorithm
The E and M Steps of EM
Theory of the EM Algorithm
Extensions of EM
Hybrid Maximization Methods
Large-Sample Inference Based on Maximum Likelihood Estimates
Standard Errors Based on the Information Matrix
Standard Errors via Methods that do not Require Computing and Inverting an Estimate of the Observed Information Matrix
Bayes and Multiple Imputation
Bayesian Iterative Simulation Methods
Multiple Imputation
Likelihood-Based Approaches to the Analysis of Incomplete Data: Some Examples
Multivariate Normal Examples, Ignoring the Missing-Data Mechanism
Inference for a Mean Vector and Covariance Matrix with Missing Data Under Normality
Estimation with a Restricted Covariance Matrix
Multiple Linear Regression
A General Repeated-Measures Model with Missing Data
Time Series Models
Robust Estimation
Robust Estimation for a Univariate Sample
Robust Estimation of the Mean and Covariance Matrix
Further Extensions of the t Model
Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism
Factored Likelihoods for Monotone Multinomial Data
ML and Bayes Estimation for Multinomial Samples with General Patterns of Missing Data
Loglinear Models for Partially Classified Contingency Tables
Mixed Normal and Non-normal Data with Missing Values, Ignoring the Missing-Data Mechanism
The General Location Model
The General Location Model with Parameter Constraints
Regression Problems Involving Mixtures of Continuous and Categorical Variables
Further Extensions of the General Location Model
Nonignorable Missing-Data Models
Likelihood Theory for Nonignorable Models
Models with Known Nonignorable Missing-Data Mechanisms: Grouped and Rounded Data
Normal Selection Models
Normal Pattern-Mixture Models
Nonignorable Models for Normal Repeated-Measures Data
Nonignorable Models for Categorical Data
Author Index
Subject Index
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