Raghunathan Missing Data Analysis in Practice

1. Auflage 2015
ISBN: 978-1-4822-1193-1
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 230 Seiten

Reihe: Chapman & Hall/CRC Interdisciplinary Statistics

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.

The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.

Zielgruppe

Researchers and graduate students in statistics, biostatistics, medical and health research, and the social and behavioral sciences.

Autoren/Hrsg.

Raghunathan, Trivellore

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Basic Concepts
Introduction
Definition of Missing Values
Missing Data Pattern
Missing Data Mechanism
Problems with Complete-Case Analysis
Analysis Approaches
Basic Statistical Concepts
A Chuckle or Two

Weighting Methods
Motivation
Adjustment Cell Method
Response Propensity Model
Example
Impact of Weights on Population Mean Estimates
Post-Stratification
Survey Weights
Alternative to Weighted Analysis
Inverse Probability Weighting

Imputation
Generation of Plausible Values
Hot Deck Imputation
Model Based Imputation
Example
Sequential Regression Imputation

Multiple Imputation
Introduction
Basic Combining Rule
Multivariate Hypothesis Testing
Combining Test Statistics
Basic Theory of Multiple Imputation
Extended Combining Rules
Some Practical Issues
Revisiting Examples
Example: St. Louis Risk Research Project

Regression Analysis
General Observations
Revisiting St. Louis Risk Research Example
Analysis of Variance
Survival Analysis Example

Longitudinal Analysis with Missing Values
Introduction
Imputation Model Assumption
Example
Practical Issues
Weighting Methods
Binary Example

Nonignorable Missing Data Mechanisms
Modeling Framework
EM-Algorithm
Inference under Selection Model
Inference under Mixture Model
Example
Practical Considerations

Other Applications
Measurement Error
Combining Information from Multiple Data Sources
Bayesian Inference from Finite Population
Causal Inference
Disclosure Limitation

Other Topics
Uncongeniality and Multiple Imputation
Multiple Imputation for Complex Surveys
Missing Values by Design
Replication Method for Variance Estimation
Final Thoughts

Bibliography

Index

Bibliographic Notes and Exercises appear at the end of each chapter.

Über Autor(innen)

Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.