Statistical Methods for Handling Incomplete Data - Jae Kwang Kim

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Auteur(s) : Jae Kwang Kim - Jun Shao

Editeur : Taylor & Francis

Langue : Anglais

Parution : 01/07/2013

Format : Moyen, de 350g à 1kg

Nombre de pages : 223

Expédition : 521

Dimensions : 24.1 x 16.1 x 2.3

ISBN : 1439849633

Résumé :

Introduction. Likelihood-Based Approach. Computation. Imputation. Propensity Scoring Approach. Nonignorable Missing Data. Longitudinal and Clustered Data. Application to Survey Sampling. Statistical Matching. Bibliography. Index.

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Sommaire:
Introduction Introduction Outline How to Use This Book Likelihood-Based Approach Introduction Observed Likelihood Mean Score Approach Observed Information Computation Introduction Factoring Likelihood Approach EM Algorithm Monte Carlo Computation Monte Carlo EM Data Augmentation Imputation Introduction Basic Theory for Imputation Variance Estimation after Imputation Replication Variance Estimation Multiple Imputation Fractional Imputation Propensity Scoring Approach Introduction Regression Weighting Method Propensity Score Method Optimal Estimation Doubly Robust Method Empirical Likelihood Method Nonparametric Method Nonignorable Missing Data Nonresponse Instrument Conditional Likelihood Approach Generalized Method of Moments (GMM) Approach Pseudo Likelihood Approach Exponential Tilting (ET) Model Latent Variable Approach Callbacks Capture-Recapture (CR) Experiment Longitudinal and Clustered Data Ignorable Missing Data Nonignorable Monotone Missing Data Past-Value-Dependent Missing Data Random-Effect-Dependent Missing Data Application to Survey Sampling Introduction Calibration Estimation Propensity Score Weighting Method Fractional Imputation Fractional Hot Deck Imputation Imputation for Two-Phase Sampling Synthetic Imputation Statistical Matching Introduction Instrumental Variable Approach Measurement Error Models Causal Inference Bibliography Index