Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (inbunden)
Inbunden (Hardback)
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John Wiley & Sons Inc
235 x 158 x 30 mm
826 g
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52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam
Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (inbunden)

Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives

An Essential Journey with Donald Rubin's Statistical Family

Inbunden Engelska, 2004-07-01
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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.
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"I congratulate the editors on this volume; it really is an essential and very enjoyable journey with Don Rubina s statistical family." (Biometrics, September 2006) "!contains much current important work!" (Technometrics, November 2005) "This a useful reference book on an important topic with applications to a wide range of disciplines." (CHOICE, September 2005) "With this variety of papers, the reader is bound to find some papers interesting!" (Journal of Applied Statistics, Vol.32, No.3, April 2005) "I strongly recommend that libraries have a copy of this book in their reference section." (Journal of the Royal Statistical Society Series A, June 2005) "...a very useful addition to academic libraries!" (Short Book Reviews, Vol.24, No.3, December 2004)

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Andrew Gelman is Professor of Statistics and Professor of Political Science at Columbia University. He has published over 150 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. His other books are Bayesian Data Analysis (1995, second edition 2003) and Teaching Statistics: A Bag of Tricks (2002).


Preface. I Casual inference and observational studies. 1 An overview of methods for causal inference from observational studies, by Sander Greenland. 1.1 Introduction. 1.2 Approaches based on causal models. 1.3 Canonical inference. 1.4 Methodologic modeling. 1.5 Conclusion. 2 Matching in observational studies, by Paul R. Rosenbaum. 2.1 The role of matching in observational studies. 2.2 Why match? 2.3 Two key issues: balance and structure. 2.4 Additional issues. 3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia. 3.1 Introduction. 3.2 Identifying and estimating the average treatment effect. 3.3 The NSWdata. 3.4 Propensity score estimates. 3.5 Conclusions. 4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams. 4.1 Methods. 4.2 Results. 4.3 Study limitations. 4.4 Conclusions and policy implications. 5 A comparison of experimental and observational data analyses, by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto. 5.1 Experimental sample. 5.2 Constructed observational study. 5.3 Concluding remarks. 6 Fixing broken experiments using the propensity score, by Bruce Sacerdote. 6.1 Introduction. 6.2 The lottery data. 6.3 Estimating the propensity scores. 6.4 Results. 6.5 Concluding remarks. 7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens. 7.1 Introduction. 7.2 The basic framework. 7.3 Bias removal using the GPS. 7.4 Estimation and inference. 7.5 Application: the Imbens--Rubin--Sacerdote lottery sample. 7.6 Conclusion. 8 Causal inference with instrumental variables, by Junni L. Zhang. 8.1 Introduction. 8.2 Key assumptions for the LATE interpretation of the IV estimand. 8.3 Estimating causal effects with IV. 8.4 Some recent applications. 8.5 Discussion. 9 Principal stratification, by Constantine E. Frangakis. 9.1 Introduction: partially controlled studies. 9.2 Examples of partially controlled studies. 9.3 Principal stratification. 9.4 Estimands. 9.5 Assumptions. 9.6 Designs and polydesigns. II Missing data modeling. 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues, by John L. Eltinge. 10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies. 10.2 Constraints. 10.3 Complex estimand structures, inferential goals, and utility functions. 10.4 Robustness. 10.5 Closing remarks. 11 Bridging across changes in classification systems, by Nathaniel Schenker. 11.1 Introduction. 11.2 Multiple imputation to achieve comparability of industry and occupation codes. 11.3 Bridging the transition from single--race reporting to multiple--race reporting. 11.4 Conclusion. 12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky. 12.1 Introduction. 12.2 Models. 12.3 Inference. 12.4 Simulation evaluations. 12.5 Conclusion. 13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan. 13.1 Introduction. 13.2 Full synthesis. 13.3 SMIKe andMIKe. 13.4 Analysis of synthetic samples. 13.5 An application. 13.6 Conclusions. 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas. 14.1 Introduction. 14.2 Statistical methods in NAEP. 14.3 Split and balanced designs for estimating population parameters. 14.4 Maximum likelihood estimation. 14.5 The role of secondary covariates. 14.6 Conclusions. 15 Propensity score estimation with missing data, by Ralph B. D'Agostino Jr. 15.1 Introduction. 15.2 Notation. 15.3 Applied example:March of Dimes data. 15.4 Conclusion and future directions. 16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan. 16.1 Missing data in clinical trials. 16.2 Ignorability and bias. 16.3 A nonignorable selection model. 16.4 Sensitivity of the mean and variance. 16.5 Sensitivity of the power. 16.6 Sensitivity of the coverage probabil