Statistical Analysis with Missing Data by Donald B. Rubin (English) Hardcover Bo

Description: FREE SHIPPING UK WIDE Statistical Analysis with Missing Data by Donald B. Rubin, Roderick J.A. Little Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description An up-to-date, comprehensive treatment of a classic text on missing data in statisticsThe 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 subjectFeatures over 150 exercises (including many new ones)Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methodsRevises previous topics based on past student feedback and class experienceContains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI)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. Back Cover 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. Flap 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. Author Biography Roderick J. A. Little, PhD., is Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan. Donald B. Rubin, PhD., is Professor, Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Department of Statistical Science, Fox School of Business at Temple University; and Professor Emeritus, Harvard University. Table of Contents Preface to the Third Edition xi Part I Overview and Basic Approaches 1 1 Introduction 3 1.1 The Problem of Missing Data 3 1.2 Missingness Patterns and Mechanisms 8 1.3 Mechanisms That Lead to Missing Data 13 1.4 A Taxonomy of Missing Data Methods 23 2 Missing Data in Experiments 29 2.1 Introduction 29 2.2 The Exact Least Squares Solution with Complete Data 30 2.3 The Correct Least Squares Analysis with Missing Data 32 2.4 Filling in Least Squares Estimates 33 2.4.1 Yatess Method 33 2.4.2 Using a Formula for the Missing Values 34 2.4.3 Iterating to Find the Missing Values 34 2.4.4 ANCOVA with Missing Value Covariates 35 2.5 Bartletts ANCOVA Method 35 2.5.1 Useful Properties of Bartletts Method 35 2.5.2 Notation 36 2.5.3 The ANCOVA Estimates of Parameters and Missing Y-Values 36 2.5.4 ANCOVA Estimates of the Residual Sums of Squares and the Covariance Matrix of Details ISBN0470526793 ISBN-10 0470526793 ISBN-13 9780470526798 Format Hardcover Publisher John Wiley & Sons Inc Year 2019 Language English Edition 3rd Series Number 793 Publication Date 2019-05-24 UK Release Date 2019-05-24 Place of Publication New York Country of Publication United States Pages 462 Replaces 9780471183860 NZ Release Date 2019-04-05 Illustrations Charts: 1 B&W, 0 Color; Tables: 60 B&W, 0 Color; Graphs: 25 B&W, 0 Color Author Roderick J.A. Little Edition Description 3rd edition Series Wiley Series in Probability and Statistics Imprint John Wiley & Sons Inc DEWEY 519.54 Audience Professional & Vocational US Release Date 2019-05-24 AU Release Date 2019-04-04 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! 30 DAY RETURN POLICY No questions asked, 30 day returns! FREE DELIVERY No matter where you are in the UK, delivery is free. SECURE PAYMENT Peace of mind by paying through PayPal and eBay Buyer Protection TheNile_Item_ID:51826146;

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