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

Edition Third edition Published Hoboken, NJ : Wiley, 2020

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Description 1 online resource Series Wiley series in probability and statistics Contents Intro; Statistical Analysis with Missing Data; Contents; Preface to the Third Edition; Part I Overview and Basic Approaches; 1 Introduction; 1.1 The Problem of Missing Data; 1.2 Missingness Patterns and Mechanisms; 1.3 Mechanisms That Lead to Missing Data; 1.4 A Taxonomy of Missing Data Methods; Problems; Note; 2 Missing Data in Experiments; 2.1 Introduction; 2.2 The Exact Least Squares Solution with Complete Data; 2.3 The Correct Least Squares Analysis with Missing Data; 2.4 Filling in Least Squares Estimates; 2.4.1 Yatess Method; 2.4.2 Using a Formula for the Missing Values 2.4.3 Iterating to Find the Missing Values2.4.4 ANCOVA with Missing Value Covariates; 2.5 Bartletts ANCOVA Method; 2.5.1 Useful Properties of Bartletts Method; 2.5.2 Notation; 2.5.3 The ANCOVA Estimates of Parameters and Missing Y-Values; 2.5.4 ANCOVA Estimates of the Residual Sums of Squares and the Covariance Matrix of; 2.6 Least Squares Estimates of Missing Values by ANCOVA Using Only Complete-Data Methods; 2.7 Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares; 2.8 Correct Least-Squares Sums of Squares with More Than One Degree of Freedom Problems3 Complete-Case and Available-Case Analysis, Including Weighting Methods; 3.1 Introduction; 3.2 Complete-Case Analysis; 3.3 Weighted Complete-Case Analysis; 3.3.1 Weighting Adjustments; 3.3.2 Poststratification and Raking to Known Margins; 3.3.3 Inference from Weighted Data; 3.3.4 Summary of Weighting Methods; 3.4 Available-Case Analysis; Problems; 4 Single Imputation Methods; 4.1 Introduction; 4.2 Imputing Means from a Predictive Distribution; 4.2.1 Unconditional Mean Imputation; 4.2.2 Conditional Mean Imputation; 4.3 Imputing Draws from a Predictive Distribution 4.3.1 Draws Based on Explicit Models4.3.2 Draws Based on Implicit Models-Hot Deck Methods; 4.4 Conclusion; Problems; 5 Accounting for Uncertainty from Missing Data; 5.1 Introduction; 5.2 Imputation Methods that Provide Valid Standard Errors from a Single Filled-in Data Set; 5.3 Standard Errors for Imputed Data by Resampling; 5.3.1 Bootstrap Standard Errors; 5.3.2 Jackknife Standard Errors; 5.4 Introduction to Multiple Imputation; 5.5 Comparison of Resampling Methods and Multiple Imputation; Problems; Part II Likelihood-Based Approaches to the Analysis of Data with Missing Values 6 Theory of Inference Based on the Likelihood Function6.1 Review of Likelihood-Based Estimation for Complete Data; 6.1.1 Maximum Likelihood Estimation; 6.1.2 Inference Based on the Likelihood; 6.1.3 Large Sample Maximum Likelihood and Bayes Inference; 6.1.4 Bayes Inference Based on the Full Posterior Distribution; 6.1.5 Simulating Posterior Distributions; 6.2 Likelihood-Based Inference with Incomplete Data; 6.3 A Generally Flawed Alternative to Maximum Likelihood: Maximizing over the Parameters and the Missing Data; 6.3.1 The Method; 6.3.2 Background; 6.3.3 Examples 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 Bibliography Includes bibliographical references and index Includes bibliographical references and indexes Notes Print version record and CIP data provided by publisher Subject Mathematical statistics. Mathematical statistics -- Problems, exercises, etc Missing observations (Statistics) Missing observations (Statistics) -- Problems, exercises, etc MATHEMATICS -- Applied. MATHEMATICS -- Probability & Statistics -- General. Mathematical statistics. Missing observations (Statistics) Genre/Form exercise books. Problems and exercises. Problems and exercises. Problèmes et exercices. Form Electronic book Author Rubin, Donald B., author. LC no. 2018061330 ISBN 9781118596012 1118596013 9780470526798 0470526793 1118595696 9781119482260 1119482267 9781118595695

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