Statistical analysis with missing data / Roderick J.A. Little, Donald B. Rubin.

Format:

Book

Author/Creator:

Little, Roderick J. A.

Contributor:

Rubin, Donald B.

Albert E. Visk, W'28, Memorial Book Fund.

Anne and Joseph Trachtman Memorial Book Fund.

Series:

Wiley series in probability and statistics

Language:

English

Subjects (All):

Mathematical statistics.

Missing observations (Statistics).

Physical Description:

xv, 381 pages : illustrations ; 25 cm.

Edition:

Second edition.

Place of Publication:

Hoboken, N.J. : Wiley, [2002]

Summary:

* Emphasizes the latest trends in the field. * Includes a new chapter on evolving methods. * Provides updated or revised material in most of the chapters.

Contents:

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

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

Futher 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.

Notes:

Includes bibliographical references (pages 349-364) and indexes.

Local Notes:

Acquired for the Penn Libraries with assistance from the Anne and Joseph Trachtman Memorial Book Fund.

Acquired for the Penn Libraries with assistance from the Albert E. Visk, W'28, Memorial Book Fund.

Other Format:

Online version: Little, Roderick J.A. Statistical analysis with missing data.

ISBN:

0471183865

9780471183860

OCLC:

48466746

Publisher Number:

99942974149

Online:

Contributor biographical information

Publisher description