Books+ Search Results
Statistical analysis with missing data
Title
Statistical analysis with missing data / Roderick J.A. Little, Donald B. Rubin.
ISBN
9781118595695
1118595696
9780470526798
Edition
Third edition.
Publication
Hoboken, NJ : John Wiley and Sons, Inc. : Wiley, 2020.
Physical Description
1 online resource.
Local Notes
Access is available to the Yale community.
Notes
Includes index.
Access and use
Access restricted by licensing agreement.
Summary
AN UP-TO-DATE, COMPREHENSIVE TREATMENT OF A CLASSIC TEXT ON MISSING DATA IN STATISTICS The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated "classic" written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.
Variant and related titles
O'Reilly Safari. OCLC KB.
Other formats
Print version: Little, Roderick J. A. Statistical analysis with missing data. 3rd edition. Hoboken, NJ : John Wiley & Sons, Inc., 2020
Format
Books / Online
Language
English
Added to Catalog
January 14, 2020
Series
Wiley series in probability and statistics
Bibliography
Includes bibliographical references and indexes.
Contents
Part I Overview and Basic Approaches
Introduction
Missing Data in Experiments
Complete-Case and Available-Case Analysis
Single Imputation Methods
Accounting for Uncertainty from Missing Data
Part II Likelihood-Based Approaches to the Analysis of Data with Missing Values
Theory of Inference Based on the Likelihood Function
Factored Likelihood Methods When the Missingness Machanism is Ignorable
Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse
Large-Sample Inference Based on Maximum Likelihood Estimates
Bayes and Multiple Imputation
Part III Likelihood-Based Approaches to the Analysis of Incomplete Data: Some Examples
Multivariate Normal Examples, Ignoring the Missingness Mechanism
Models for Robust Estimation
Models for Partially Classified Contingency Tables, Ignorning the Missingness Mechanism
Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missingness Mechanism
Missing Not at Random Models.
Subjects
Genre/Form
Problems and exercises.
Also listed under
Bookmark As
Citation
