Screen LibraryStatistical analysis with missing data /
Praise for the First Edition of Statistical Analysis with Missing Data ""An important contribution to the applied statistics literature.... I give the book high marks for unifying and making accessible much of the past and current work in this important area.""-William E. Strawde...
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| Main Authors: | |
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| Group Author: | |
| Published: |
Wiley,
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| Publisher Address: | Hoboken : |
| Publication Dates: |
[2002] ©2002 |
| Literature type: | eBook |
| Language: | English |
| Edition: | Second edition. |
| Series: |
Wiley Series in Probability and Statistics
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| Subjects: | |
| Online Access: |
http://onlinelibrary.wiley.com/book/10.1002/9781119013563 |
| Summary: |
Praise for the First Edition of Statistical Analysis with Missing Data ""An important contribution to the applied statistics literature.... I give the book high marks for unifying and making accessible much of the past and current work in this important area.""-William E. Strawderman, Rutgers University ""This book...provide[s] interesting real-life examples, stimulating end-of-chapter exercises, and up-to-date references. It should be on every applied statistician's bookshelf.""-The Statistician ""The book should be studied in the statistical methods department in every statistical agency." |
| Item Description: | 6.2. Likelihood-based Inference with Incomplete Data |
| Carrier Form: | 1 online resource (xv, 381 pages) : illustrations. |
| Bibliography: | Includes bibliographical references (pages 349-364) and indexes. |
| ISBN: |
9781118625866 1118625862 9781119013563 1119013569 |
| Index Number: | QA276 |
| CLC: | O212 |
| Contents: |
Cover ; Title Page ; Copyright ; Contents ; Preface ; Part I: Overview and Basic Approaches ; Chapter 1: Introduction ; 1.1. The Problem of Missing Data ; 1.2. Missing-data Patterns ; 1.3. Mechanisms That Lead to Missing Data ; 1.4. A Taxonomy of Missing-data Methods ; Chapter 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. Yates's Method ; 2.4.2. Using a Formula for the Missing Values 2.4.3. Iterating to Find the Missing Values 2.4.4. Ancova with Missing-value Covariates ; 2.5. Bartlett's Ancova Method ; 2.5.1. Useful Properties of Bartlett's 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 ß^ ; 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 Chapter 3: 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. Added Variance from Nonresponse Weighting ; 3.3.3. Post-stratification and Raking to Known Margins ; 3.3.4. Inference from Weighted Data ; 3.3.5. Summary of Weighting Methods ; 3.4. Available-case Analysis ; Chapter 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 Models ; 4.3.2. Draws Based on Implicit Models ; 4.4. Conclusions ; Chapter 5: Estimation of Imputation Uncertainty ; 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 ; Part II: Likelihood-based Approaches to the Analysis of Missing Data ; Chapter 6: Theory of Inference Based on the Likelihood Function ; 6.1. Review of Likelihood-based Estimation for Complete Data ; 6.1.1. Maximum Likelihood Estimation ; 6.1.2. Rudiments of Bayes Estimation ; 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 Draws from Posterior Distributions |