Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

An Essential Journey with Donald Rubin's Statistical Family

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Beschreibung

<p><i><b>Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family</b></i></p> <p>This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data.</p> <p>Key features of the book include:</p> <ul> <li>Comprehensive coverage of an imporant area for both research and applications.</li> <li>Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.</li> <li>Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.</li> <li>Includes a number of applications from the social and health sciences.</li> <li>Edited and authored by highly respected researchers in the area.</li> </ul>

Artikeldetails

EAN

9780470090435

Sprache

Englisch

Einband / Art

Buch

Maße

240 x 161 x 28 mm

Erscheinungsjahr

2004

Verlag / Hersteller

John Wiley & Sons