Table of Contents:
“...Chapter 9 Methods
Based on Models for Domain Knowledge -- 9.1 Correction with
Data Modifying Rules -- 9.1.1 Modifying Functions -- 9.1.2 A Class of Modifying Functions on Numerical
Data -- 9.1.2 Exercises for Section -- 9.2 Rule‐
Based Correction with dcmodify -- 9.2.1 Reading Rules from File -- 9.2.2 Modifying Rule Syntax -- 9.2.3 Missing Values -- 9.2.4 Sequential and Sequence‐Independent Execution -- 9.2.5 Options Settings Management -- 9.3 Deductive Correction -- 9.3.1 Correcting Typing Errors in Numeric
Data -- 9.3.1 Exercises for Section -- 9.3.2 Deductive Imputation Using Linear Restrictions -- Chapter 10 Imputation and Adjustment -- 10.1 Missing
Data -- 10.1.1 Missing
Data Mechanisms -- 10.1.2 Visualizing and Testing for Patterns in Missing
Data Using R -- 10.2 Model‐
Based Imputation -- 10.3 Model‐
Based Imputation in R -- 10.3.1 Specifying Imputation Methods with simputation -- 10.3.2 Linear Regression‐
Based Imputation -- 10.3.3 M‐Estimation -- 10.3.4 Lasso, Ridge, and Elasticnet Regression -- 10.3.5 Classification and Regression Trees -- 10.3.6 Random Forest -- 10.4 Donor Imputation with R -- 10.4.1 Random and Sequential Hot Deck Imputation -- 10.4.2 k Nearest Neighbors and Predictive Mean Matching -- 10.5 Other Methods in the simputation Package -- 10.6 Imputation
Based on the EM Algorithm -- 10.6.1 The EM Algorithm -- 10.6.2 EM Imputation Assuming the Multivariate Normal Distribution -- 10.7 Sampling Variance under Imputation -- 10.8 Multiple Imputations -- 10.8.1 Multiple Imputation
Based on the EM Algorithm -- 10.8.2 The Amelia Package -- 10.8.3 Multivariate Imputation with Chained Equations (Mice) -- 10.8.4 Imputation with the mice Package -- 10.9 Analytic Approaches to Estimate Variance of Imputation -- 10.9.1 Imputation as Part of the Estimator -- 10.10 Choosing an Imputation Method -- 10.11 Constraint Value Adjustment....
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