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Books Books 1 - 10 of about 45 related to Multiple Imputation for Nonresponse in Surveys.   

Multiple Imputation for Nonresponse in Surveys

Donald B. Rubin - Mathematics - 2009 - 288 pages
Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the ...
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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

Donald B. Rubin, Andrew Gelman, Xiao-Li Meng - Mathematics - 2004 - 407 pages
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental ...
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Bayesian Data Analysis, Second Edition

Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin - Mathematics - 2003 - 696 pages
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to ...
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Matched Sampling for Causal Effects

Donald B. Rubin - Mathematics - 2006
Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted ...
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Bayesian Data Analysis, Third Edition

Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin - Mathematics - 2013 - 675 pages
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and ...
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Statistical Analysis with Missing Data

Roderick J. A. Little, Donald B. Rubin - Mathematics - 2014 - 400 pages
Praise for the First Edition of Statistical Analysis with Missing Data "An important contribution to the applied statistics literature.... I give the book high marks for ...
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Causal Inference in Statistics, Social, and Biomedical Sciences

Guido W. Imbens, Donald B. Rubin - Business & Economics - 2015 - 592 pages
This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.
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