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…mehr
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.
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).
Inhaltsangabe
Preface. I Casual inference and observational studies. 1 An overview of methods for causal inference from observational studies by Sander Greenland. 2 Matching in observational studies by Paul R. Rosenbaum. 3 Estimating causal effects in nonexperimental studies by Rajeev Dehejia. 4 Medication cost sharing and drug spending in Medicare by Alyce S. Adams. 5 A comparison of experimental and observational data analyses by Jennifer L. Hill Jerome P. Reiter and Elaine L. Zanutto. 6 Fixing broken experiments using the propensity score by Bruce Sacerdote. 7 The propensity score with continuous treatments by Keisuke Hirano and Guido W. Imbens. 8 Causal inference with instrumental variables by Junni L. Zhang. 9 Principal stratification by Constantine E. Frangakis. II Missing data modeling. 10 Nonresponse adjustment in government statistical agencies: constraints inferential goals and robustness issues by John L. Eltinge. 11 Bridging across changes in classification systems by Nathaniel Schenker. 12 Representing the Census undercount by multiple imputation of households by Alan M. Zaslavsky. 13 Statistical disclosure techniques based on multiple imputation by Roderick J. A. Little Fang Liu and Trivellore E. Raghunathan. 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress by Neal Thomas. 15 Propensity score estimation with missing data by Ralph B. D'Agostino Jr. 16 Sensitivity to nonignorability in frequentist inference by Guoguang Ma and Daniel F. Heitjan. III Statistical modeling and computation. 17 Statistical modeling and computation by D. Michael Titterington. 18 Treatment effects in before-after data by Andrew Gelman. 19 Multimodality in mixture models and factor models by Eric Loken. 20 Modeling the covariance and correlation matrix of repeated measures by W. John Boscardin and Xiao Zhang. 21 Robit regression: a simple robust alternative to logistic and probit regression by Chuanhai Liu. 22 Using EM and data augmentation for the competing risks model by Radu V. Craiu and Thierry Duchesne. 23 Mixed effects models and the EM algorithm by Florin Vaida Xiao-Li Meng and Ronghui Xu. 24 The sampling/importance resampling algorithm by Kim-Hung Li. IV Applied Bayesian inference. 25 Whither applied Bayesian inference? by Bradley P. Carlin. 26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics by David A. van Dyk and Taeyoung Park. 27 Improved predictions of lynx trappings using a biological model by Cavan Reilly and Angelique Zeringue. 28 Record linkage using finite mixture models by Michael D. Larsen. 29 Identifying likely duplicates by record linkage in a survey of prostitutes by Thomas R. Belin Hemant Ishwaran Naihua Duan Sandra H. Berry and David E. Kanouse. 30 Applying structural equation models with incomplete data by Hal S. Stern and Yoonsook Jeon. 31 Perceptual scaling by Ying Nian Wu Cheng-En Guo and Song Chun Zhu. References. Index.
Preface. I Casual inference and observational studies. 1 An overview of methods for causal inference from observational studies by Sander Greenland. 2 Matching in observational studies by Paul R. Rosenbaum. 3 Estimating causal effects in nonexperimental studies by Rajeev Dehejia. 4 Medication cost sharing and drug spending in Medicare by Alyce S. Adams. 5 A comparison of experimental and observational data analyses by Jennifer L. Hill Jerome P. Reiter and Elaine L. Zanutto. 6 Fixing broken experiments using the propensity score by Bruce Sacerdote. 7 The propensity score with continuous treatments by Keisuke Hirano and Guido W. Imbens. 8 Causal inference with instrumental variables by Junni L. Zhang. 9 Principal stratification by Constantine E. Frangakis. II Missing data modeling. 10 Nonresponse adjustment in government statistical agencies: constraints inferential goals and robustness issues by John L. Eltinge. 11 Bridging across changes in classification systems by Nathaniel Schenker. 12 Representing the Census undercount by multiple imputation of households by Alan M. Zaslavsky. 13 Statistical disclosure techniques based on multiple imputation by Roderick J. A. Little Fang Liu and Trivellore E. Raghunathan. 14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress by Neal Thomas. 15 Propensity score estimation with missing data by Ralph B. D'Agostino Jr. 16 Sensitivity to nonignorability in frequentist inference by Guoguang Ma and Daniel F. Heitjan. III Statistical modeling and computation. 17 Statistical modeling and computation by D. Michael Titterington. 18 Treatment effects in before-after data by Andrew Gelman. 19 Multimodality in mixture models and factor models by Eric Loken. 20 Modeling the covariance and correlation matrix of repeated measures by W. John Boscardin and Xiao Zhang. 21 Robit regression: a simple robust alternative to logistic and probit regression by Chuanhai Liu. 22 Using EM and data augmentation for the competing risks model by Radu V. Craiu and Thierry Duchesne. 23 Mixed effects models and the EM algorithm by Florin Vaida Xiao-Li Meng and Ronghui Xu. 24 The sampling/importance resampling algorithm by Kim-Hung Li. IV Applied Bayesian inference. 25 Whither applied Bayesian inference? by Bradley P. Carlin. 26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics by David A. van Dyk and Taeyoung Park. 27 Improved predictions of lynx trappings using a biological model by Cavan Reilly and Angelique Zeringue. 28 Record linkage using finite mixture models by Michael D. Larsen. 29 Identifying likely duplicates by record linkage in a survey of prostitutes by Thomas R. Belin Hemant Ishwaran Naihua Duan Sandra H. Berry and David E. Kanouse. 30 Applying structural equation models with incomplete data by Hal S. Stern and Yoonsook Jeon. 31 Perceptual scaling by Ying Nian Wu Cheng-En Guo and Song Chun Zhu. References. Index.
Rezensionen
"I congratulate the editors on this volume; it really is an essential and very enjoyable journey with Don Rubin 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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