Multiple Imputation of Missing Data in Practice

Multiple Imputation of Missing Data in Practice

Basic Theory and Analysis Strategies

Hsu, Chiu-Hsieh; Zhang, Guangyu; He, Yulei

Taylor & Francis Ltd

05/2024

476

Mole

9781032136899

Pré-lançamento - envio 15 a 20 dias após a sua edição

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1. Introduction. 2. Statistical Background. 3. Multiple Imputation Analysis: Basics. 4. Multiple Imputation for Univariate Missing Data: Parametric Methods. 5. Multiple Imputation for Univariate Missing Data: Robust Methods. 6. Multiple Imputation for Multivariate Missing Data: the Joint Modeling Approach. 7. Multiple Imputation for Multivariate Missing Data: the Fully Conditional Specification Approach. 8. Multiple Imputation in Survival Data Analysis. 9. Multiple Imputation for Longitudinal Data. 10. Multiple Imputation Analysis for Complex Survey Data. 11. Multiple Imputation for Data Subject to Measurement Error. 12. Multiple Imputation Diagnostics.
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Multiple Imputation;Fully Conditional Specification Approach;Missing Data;Survival Data Analysis;Model Imputation;Complex Survey Data;Multiple Imputation Analysis;Longitudinal Data;Complete Data Model;Multivariate missing data;Multiply Imputed Data;Univariate missing data;Imputed Values;Imputation Algorithm;Posterior Predictive Distribution;Missingness Mechanism;Posterior Distribution;DA Algorithm;Normal Linear Regression Model;MAR Assumption;Missing Values;Imputation Methods;SAS Proc Mi;Scatter Plots;Propensity Score;Missing Data Problems;Multivariate Linear Mixed Model;Aft Model;Imputation Strategy;NHANES Iii;Pattern Mixture Model