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. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.HINKELMAN and KEMPTHORNE: Am Design and Analysis of Experiments, Volume 1: Introduction to Experimental Design HOAGLIN, ... Models: Regression and the Analysis of Variance, Second Edition HOEL Am Introduction to Mathematical Statistics, Fifth Edition HOGG and ... PANJER, and WILLMOT Am Solutions Manual to Accompany Loss Models: From Data to Decisions aNow available in a lower pricedanbsp;...
|Title||:||Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives|
|Author||:||Andrew Gelman, Xiao-Li Meng|
|Publisher||:||John Wiley & Sons - 2004-10-22|