Zum Hauptinhalt springen Zur Suche springen Zur Hauptnavigation springen
Beschreibung
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.

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.

Zusammenfassung
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.
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.
Details
Erscheinungsjahr: 2020
Genre: Biologie
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Inhalt: Einband - flex.(Paperback)
ISBN-13: 9780367737665
ISBN-10: 0367737663
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Raghunathan, Trivellore
Hersteller: Taylor & Francis
Chapman and Hall/CRC
Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de
Maße: 12 x 156 x 234 mm
Von/Mit: Trivellore Raghunathan
Erscheinungsdatum: 18.12.2020
Gewicht: 0,362 kg
Artikel-ID: 133315355