Missing Data Analysis in Practice

1. Auflage 2015
ISBN: 978-1-4822-1192-4
Verlag: CRC Press

Buch, Englisch, 230 Seiten, Format (B × H): 162 mm x 244 mm, Gewicht: 468 g

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. Weighting Methods. Imputation. Multiple Imputation. Regression Analysis. Longitudinal Analysis with Missing Values. Nonignorable Missing Data Mechanisms. Other Applications. Other Topics. Bibliography. Index.

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.