Statistical analysis with missing data. (2020)

• Record Type: Book
• Further Information: Note: Roderick J.A. Little, Donald B. Rubin.
• Authors: Little, Roderick J. A; Rubin, Donald B
• Contents: Preface Part I: Overview and Basic Approaches Chapter 1. Introduction 1.1. The Problem of Missing Data Example 1.1. Nonresponse for a Binary Outcome Measured at Three Times Points. Example 1.2. Causal Effects of Treatments with Survival and Quality of Life Outcomes. Example 1.3. Nonresponse in Opinion Polls. 1.2. Missingness Patterns and Mechanisms Example 1.4. Univariate Missing Data. Example 1.5. Unit and Item Nonresponse in Surveys. Example 1.6. Attrition in Longitudinal Studies. Example 1.7. The File-Matching Problem, with Two Sets of Variables Never Jointly Observed. Example 1.8. Patterns with Latent Variables That Are Never Observed. Example 1.9. Missing Data in Clinical Trials. 1.3. Mechanisms that Lead to Missing Data Example 1.10. Artificially-Created Missing Data in a Univariate Normal Sample. Example 1.11. Right-Censored Survival Data. Example 1.12. Historical Heights. Example 1.13. MAR for Univariate Missing Data. Example 1.14. Missing Data by Design: Double and Matrix Sampling. Example 1.15. Measurement Error as a Missing-Data Problem. Example 1.16. Missing Data by Design: Disclosure Limitation. Example 1.17. Income Nonresponse. Example 1.18. Mechanisms of Attrition in Longitudinal Data (Example 1.6 continued). Example 1.19. MAR for a General Bivariate Pattern. 1.4. A Taxonomy of Missing-Data Methods Example 1.20 Estimating the Mean and Covariance Matrix with Monotone Missingness Pattern. Example 1.21. Estimating the Mean and Covariance Matrix with General Missingness Pattern. Example 1.22. Estimation When Some Variables Are Categorical. Example 1.23. Estimation When the Data May Not Be Missing at Random. 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 ˆβ 2.6. Least Squares Estimates of Missing Values by ANCOVA using only Complete-Data Methods Example 2.1. Estimating Missing Values in a Randomized Block. 2.7. Correct Least Squares Estimates of Standard Errors and One Degree of Freedom Sums of Squares Example 2.2. Adjusting Standard Errors for Filled-In Missing Values (Example 2.1 continued). 2.8. Correct Least Squares Sums of Squares with more than One Degree of Freedom Example 2.3. Adjusting Sums of Squares for the Filled-In Values (Example 2.2 continued). Chapter 3. Complete-Case and Available-Case Analysis, Including Weighting Methods 3.1. Introduction 3.2. Complete-Case Analysis Example 3.1. Efficiency of Complete-Case Analysis for Bivariate Normal Monotone Data. Example 3.2. Bias of Complete-Case Inferences for Means. Example 3.3. Bias and Precision of Complete-Case Inferences for Regression Coefficients. Example 3.4. Bias and Precision of Complete-Case Inferences for an Odds Ratio. 3.3. Weighted Complete-Case Analysis 3.3.1. Weighting Adjustments Example 3.5. Randomization Inference in Surveys with Complete Response. Example 3.6. Weighting Class Estimate of the Mean. Example 3.7. Propensity Weighting. Example 3.8. Inverse-Probability Weighted Generalized Estimating Equations. 3.3.2. Post-stratification and Raking to Known Margins Example 3.9. Post-Stratification. Example 3.10. Raking-Ratio Estimation. 3.3.3 Inference from Weighted Data 3.3.4 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 Example 4.1. Imputing Means within Adjustment Cells. Example 4.2. Regression Imputation. Example 4.3. Buck’s Method. 4.3. Imputing Draws from a Predictive Distribution 4.3.1 Draws Based on Explicit Models Example 4.4. Stochastic Regression Imputation. Example 4.5. Comparison of Methods for Bivariate Monotone MCAR Data. Example 4.6. Missing Covariates in Regression. Example 4.7 Regression Calibration for Measurement Error in Regression 4.3.2. Draws Based on Implicit Models – Hot Deck Methods. Example 4.8. The Hot Deck by Simple Random Sampling with Replacement. Example 4.9. Hot Deck within Adjustment Cells. Example 4.10. Hot Deck Based on a Matching Metric. Example 4.11. Hot Decks for Multivariate Missing Data. Example 4.12. Imputation Methods for Repeated Measures with Dropouts. 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 Dataset. Example 5.1. Standard Errors from Cluster Samples with Imputed Data. Example 5.2. Standard Errors from Stratified Cluster Samples with Imputed Data. 5.3. Standard Errors for Imputed Data by Resampling 5.3.1 Bootstrap standard errors. Example 5.3. The Simple Bootstrap for Complete Data. Example 5.4. The Simple Bootstrap Applied to Data Completed by Imputation. 5.3.2. Jackknife Standard Errors. Example 5.5. The Simple Jackknife for Complete Data. Example 5.6. The Simple Jackknife Applied to Data Completed by Imputation. Example 5.7. Standard Errors from Stratified Cluster Samples (Example 5.2 continued). 5.4. Introduction to Multiple Imputation Example 5.8. Multiple Imputation for Stratified Random Samples. 5.5. Comparison of Resampling Methods and Multiple Imputation PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF DATA WITH MISSING VALUES 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 Example 6.1. Univariate Normal Sample. Example 6.2. Exponential Sample. Example 6.3. Multinomial Sample. Example 6.4. Multivariate Normal Sample Example 6.5. Exponential Sample (Example 6.2 continued). Example 6.6. Multinomial Sample (Example 6.3 continued). Example 6.7. Univariate Normal Sample (Example 6.1 continued). Example 6.8. Multivariate Normal Sample (Example 6.4 continued). Example 6.9. A Conditional Distribution Derived from a Bivariate Normal Sample. Example 6.10. Multiple Linear Regression, Unweighted and Weighted. Example 6.11. Generalized Linear Models. Example 6.12. Normal Repeated Measures Models 6.1.2. Inference Based on the Likelihood 6.1.3. Large-Sample Maximum Likelihood and Bayes Inference Example 6.13. Exponential Sample (Example 6.2 continued). Example 6.14. Univariate Normal Sample (Example 6.1 continued). Example 6.15. Univariate Normal Sample (Example 6.1 continued). 6.1.4 Bayes Inference Based on the Full Posterior Distribution Example 6.16. Bayes Inference for a Univariate Normal Sample with Conjugate Prior (Example 6.1 continued). Example 6.17. Bayes’ Inference for Unweighted and Weighted Multiple Linear Regression (Example 6.10 continued). Example 6.18. Bayes Inference for a Multinomial Sample (Example 6.3 continued). EXAMPLE 6.19. Bayes Inference for a Multivariate Normal Sample (Example 6.4 continued). 6.1.5. Simulating Posterior Distributions Example 6.20 Bayes Inferenc
• Edition: Third edition
• Publisher Details: Hoboken, NJ : Wiley
• Publication Date: 2020
• Extent: 1 online resource
• Subjects: 519.5; Mathematical statistics; Mathematical statistics -- Problems, exercises, etc; Missing observations (Statistics); MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General; Electronic books
• Languages: English
• ISBNs: 9781118595695; 1118595696
• Related ISBNs: 9780470526798
• Notes: Note: Online resource; title from PDF title page (EBSCO, viewed March 27, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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