Format:
1 Online-Ressource (xviii, 478 Seiten)
,
Diagramme
Edition:
First edition
ISBN:
9780429156397
,
0429156391
,
9780429545672
,
0429545673
,
9780429530975
,
0429530978
,
9781498722070
,
1498722075
Series Statement:
A Chapman & Hall book
Content:
Introduction A Motivating Example Definition of Missing Data Missing Data Patterns Missing Data Mechanisms Structure of the Book Statistical Background Introduction Frequentist Theory Sampling Experiment Model, Parameter, and Estimation Hypothesis Testing Resampling Methods: the Bootstrap Approach Bayesian Analysis Rudiments Prior Distribution Bayesian Computation Asymptotic Equivalence between Frequentist and BayesianEstimates Likelihood-Based Approaches to Missing Data Analysis Ad-Hoc Missing Data Methods Use of Monte Carlo Simulation Study Summary Multiple Imputation Analysis: Basics Introduction Basic Ideas Bayesian Motivation Basic Combining Rules and Their Justifications Why Does Multiple Imputation Work Statistical Inference on Multiply Imputed Data Scalar Inference Multi-Parameter Inference How to Choose the Number of Imputations How to Create Multiple Imputations Bayesian Imputation algorithm Proper Multiple Imputation Alternative Strategies Practical Implementation Summary Multiple Imputation for Univariate Missing Data: Parametric Methods Overview Imputation for Continuous Data Based on Normal Linear ModelsImputation for Non-Continuous Data Based on GeneralizedLinear Models Generalized Linear Models Imputation for Binary Data Logistic Regression Model Imputation Discriminant Analysis Imputation Rounding Data Separation Imputation for Non-Binary Categorical Data Imputation for Other Types of Data Imputation for a Missing Covariate in a Regression Analysis Summary Multiple Imputation for Univariate Missing Data: Robust Methods Overview Data Transformation Transforming or Not How to Apply Transformation in Multiple Imputation Imputation Based on Smoothing Methods Main Idea Practical Use Adjustments for Continuous Data with Range Restrictions Predictive Mean Matching Hot-Deck Imputation Basic Idea and Procedure PMM for Non-Continuous Data Additional Discussion Inclusive Imputation Strategy Basic Idea Dual Modeling Strategy Propensity Score Calibration Estimation and Doubly RobustImputation Methods Summary Multiple Imputation for Multivariate Missing Data: the Joint Modeling Approach Introduction Imputation for Monotone Missing Data Multivariate Continuous Data Multivariate Normal Models Nonnormal Continuous Data Multivariate Categorical Data Log-Linear Models Latent Variable Models Mixed Categorical and Continuous Variables One Continuous Variable and One Binary Variable General Location Models Latent Variable Models Missing Outcome and Covariates in a Regression Analysis General Strategy Conditional Modeling Framework Using WinBUGS Background Missing Interactions and Squared Terms ofCovariates in Regression Analysis Imputation Using Flexible Distributions Summary Multiple Imputation for Multivariate Missing Data: the Fully Conditional Specification Approach Introduction Basic Idea Specification of Conditional Models Handling Complex Data Features Data Subject to Bounds or Restricted Ranges Skip Patterns Implementation General Algorithm Software Using WinBUGS Subtle Issues Compatibility Performance under Model Misspecifications A Practical Example Summary Multiple Imputation in Survival Data Analysis Introduction Imputation for Censored Data Theoretical Basis Parametric Imputation for Censored Event Times Semiparametric Imputation for Censored Event Times Merits of Imputing Censored Event Times Survival Analysis with Missing Covariates Overview Joint Modeling Fully Conditional Specification Semiparametric Methods Summary Multiple Imputation for Longitudinal Data Introduction Mixed Models for Longitudinal Data Imputation Based on Mixed Models Why Using Mixed Models General Imputation Algorithm Examples Wide Format Imputation Multilevel Data Summary Multiple Imputation Analysis for Complex Survey Data Introduction Design-Based Inference for Survey Data Imputation Strategies for Complex Survey Data General Principles Incorporating the Survey Sampling Design Assuming MAR Using FCS Modeling Options Some Examples from the Literature Database Construction and Release Data Editing Documentation and Release Summary Multiple Imputation for Data Subject to Measurement Error Introduction Rationale Imputation Strategies True Values Partially Observed Basic Setup Direct Imputation Accommodating a Specific Analysis Using FCS Predictors under Detection Limits True Values Fully Unobserved Data Harmonization Using Bridge Studies Combining Information from Multiple Data Sources Imputation for a Composite Variable Summary Multiple Imputation Diagnostics Overview Imputation Model Development Inclusion of Variables Forming Imputation Models Comparison between Observed and Imputed Values Comparison on Marginal Distributions Comparison on Conditional Distributions Basic Idea Using Propensity Score Checking Completed Data Posterior Predictive Checking Comparing Completed Data with Their Replicates Assessing the Fraction of Missing Information Relating the Fraction of Missing Information withModel Predictability Prediction Accuracy Comparison among Different Methods Summary Multiple Imputation Analysis for Nonignorable Missing Data Introduction The Implication of Missing Not at Random Using the Inclusive Imputation to Rescue Missing Not at Random Models Selection Models Pattern Mixture Models Shared Parameter Models Analysis Strategies Direct Imputation Sensitivity Analysis Summary Some Advanced Topics Overview Uncongeniality in Multiple Imputation Analysis Combining Analysis Results from Multiply Imputed Datasets:Further Considerations Normality Assumption in Question Beyond Sufficient Statistics Complicated Completed-Data Analyses: Variable SelectionHigh-Dimensional Data Final Thoughts
Additional Edition:
ISBN 9781498722063
Additional Edition:
ISBN 9781032136899
Additional Edition:
Erscheint auch als Druck-Ausgabe He, Yulei Multiple imputation of missing data in practice Boca Raton : CRC Press, 2022 ISBN 9781498722063
Additional Edition:
ISBN 9781032136899
Language:
English
Keywords:
Fehlende Daten
;
Imputationstechnik
;
Unvollkommene Information
;
Messfehler
;
Umfrage
;
Längsschnittuntersuchung
DOI:
10.1201/9780429156397
Bookmarklink
