Multiple Imputation for Nonresponse in Surveys
eBook - PDF

Multiple Imputation for Nonresponse in Surveys

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Multiple Imputation for Nonresponse in Surveys

About this book

Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.

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Yes, you can access Multiple Imputation for Nonresponse in Surveys by Donald B. Rubin in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Multiple Imputation for Nonresponse in Surveys
  2. Contents
  3. TABLES AND FIGURES
  4. GLOSSARY
  5. 1. INTRODUCTION
  6. 1.1. Overview
  7. 1.2. Examples of Surveys with Nonresponse
  8. 1.3. Properly Handling Nonresponse
  9. 1.4. Single Imputation
  10. 1.5. Multiple Imputation
  11. 1.6.Numerical Example Using Multiple Imputation
  12. 1.7. Guidance for the Reader
  13. Problems
  14. 2. STATISTICAL BACKGROUND
  15. 2.1. Introduction
  16. 2.2. Variables in the Finite Population
  17. 2.3. Probability Distributions and Related Calculations
  18. 2.4. Probability Specifications for Indicator Variables
  19. 2.5. Probability Specifications for (X, Y )
  20. 2.6. Bayesian Inference for a Population Quantity
  21. 2.7. Interval Estimation
  22. 2.8. Bayesian Procedures for Constructing Interval Estimates, Including Significance Levels and Point Estimates
  23. 2.9. Evaluating the Performance of Procedures
  24. 2.10. Similarity of Bayesian and Randomization-Based Inferences in Many Practical Cases
  25. Problems
  26. 3. UNDERLYING BAYESIAN THEORY
  27. 3.1. introduction and Summary of Repeated-Imputation Inferences
  28. 3.2. Key Results for Analysis When the Multiple Imputations Are Repeated Draws from the Posterior Distribution of the Missing Values
  29. 3.3. Inference for Scalar Estimands from a Modest Number of Repeated Completed-Data Means and Variances
  30. 3.4. Significance Levels for Multicomponent Estimands from a Modest Number of Repeated Completed-Data Means and Variance-Covariance Matrices
  31. 3.5. Significance Levels from Repeated Completed-Data Significance Levels
  32. 3.6. Relating the Completed-Data and Complete-Data Posterior Distributions When the Sampling Mechanism Is Ignorable
  33. Problems
  34. 4. RANDOMIZATION-BASED EVALUATIONS
  35. 4.1. Introduction
  36. 4.2. General Conditions for the Randomization-Validity of Infinite-m Repeated-Imputation Inferences
  37. 4.3. Examples of Proper and Improper Imputation Methods in a Simple Case with Ignorable Nonresponse
  38. 4.4. Further Discussion of Proper Imputation Methods
  39. 4.5. The Asymptotic Distribution of (Qm,Um, Bo) for Proper Imputation Methods
  40. 4.6. Evaluations of Finite-m Inferences with Scalar Estimands
  41. 4.7. Evaluation of Significance Levels from the Moment- Based Statistics Dm and Dm with Multicomponent Estimands
  42. 4.8. Evaluation of Significance Levels Based on Repeated Significance Levels
  43. Problems
  44. 5. PROCEDURES WITH IGNORABLE NONRESPONSE
  45. 5.1. Introduction
  46. 5.2. Creating Imputed Values under an Explicit Model
  47. 5.3. Some Explicit Imputation Models with Univariate yi and Covariates
  48. 5.4. Monotone Patterns of Missingness in Multivariate Yi
  49. 5.5. Missing Social Security Benefits in the Current Population Survey
  50. 5.6. Beyond Monotone Missingness
  51. Problems
  52. 6. PROCEDURES WITH NONIGNORABLE NONRESPONSE
  53. 6.1. Introduction
  54. 6.2. Nonignorable Nonresponse with Univariate yl. and No X i
  55. 6.3. Formal Tasks with Nonignorable Nonresponse
  56. 6.4. Illustrating Mixture Modeling Using Educational Testing Service Data
  57. 6.5. Illustrating Selection Modeling Using CPS Data
  58. 6.6. Extensions to Surveys with Follow-Ups
  59. 6.7. Follow-Up Response in a Survey of Drinking Behavior Among Men of Retirement Age
  60. Problems
  61. REFERENCES
  62. AUTHOR INDEX
  63. SUBJECT INDEX