Statistical Analysis with Missing Data

Name: Statistical Analysis with Missing Data
Price: 64.5 USD
Availability: InStock
ISBN: 9780471183860

ISBN-10: 0471183865

ISBN-13: 9780471183860

Edition: 2nd 2002 (Revised)

Authors: Roderick J. A. Little, Donald B. Rubin

List price: $180.00

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Description:

This new edition updates the subject and includes new missing data methods, new theoretical and technical extensions, and new developments in software for applying missing-data methods.

Book details

List price: $180.00
Edition: 2nd
Copyright year: 2002
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/9/2002
Binding: Hardcover
Pages: 408
Size: 6.25" wide x 9.75" long x 0.75" tall
Weight: 1.540
Language: English



Preface



Overview and Basic Approaches



Introduction



The Problem of Missing Data



Missing-Data Patterns



Mechanisms That Lead to Missing Data



A Taxonomy of Missing-Data Methods



Missing Data in Experiments



Introduction



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



Introduction



Complete-Case Analysis



Weighted Complete-Case Analysis



Available-Case Analysis



Single Imputation Methods



Introduction



Imputing Means from a Predictive Distribution



Imputing Draws from a Predictive Distribution



Conclusions



Estimation of Imputation Uncertainty



Introduction



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



Introduction



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



Introduction



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



Introduction



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



Introduction



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



Introduction



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



Introduction



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


References


Author Index


Subject Index