(Ebook) Applied Bayesian Modeling and Causal Inference From Incomplete-Data Perspectives by Walter A. Shewhart, Samuel S. Wilks(eds.) ISBN 9780470090435, 9780470090459, 047009043X, 0470090456

(Ebook) Applied Bayesian Modeling and Causal Inference From Incomplete-Data Perspectives by Walter A. Shewhart, Samuel S. Wilks(eds.) ISBN 9780470090435, 9780470090459, 047009043X, 0470090456

Content:
Chapter 1 An Overview of Methods for Causal Inference from Observational Studies (pages 1–13): Sander Greenland
Chapter 2 Matching in Observational Studies (pages 15–24): Paul R. Rosenbaum
Chapter 3 Estimating Causal Effects in Nonexperimental Studies (pages 25–35): Rajeev Dehejia
Chapter 4 Medication Cost Sharing and Drug Spending in Medicare (pages 37–47): Alyce S. Adams
Chapter 5 A Comparison of Experimental and Observational Data Analyses (pages 49–60): Jennifer L. Hill, Jerome P. Reiter and Elaine L. Zanutto
Chapter 6 Fixing Broken Experiments Using the Propensity Score (pages 61–71): Bruce Sacerdote
Chapter 7 The Propensity Score with Continuous Treatments (pages 73–84): Keisuke Hirano and Guido W. Imbens
Chapter 8 Causal Inference with Instrumental Variables (pages 85–96): Junni L. Zhang
Chapter 9 Principal Stratification (pages 97–108): Constantine E. Frangakis
Chapter 10 Nonresponse Adjustment in Government Statistical Agencies: Constraints, Inferential Goals, and Robustness Issues (pages 109–115): John Eltinge
Chapter 11 Bridging across Changes in Classification Systems (pages 117–128): Nathaniel Schenker
Chapter 12 Representing the Census Undercount by Multiple Imputation of Households (pages 129–140): Alan M. Zaslavsky
Chapter 13 Statistical Disclosure Techniques Based on Multiple Imputation (pages 141–152): Roderick J. A. Little, Fang Liu and Trivellore E. Raghunathan
Chapter 14 Designs Producing Balanced Missing Data: Examples from the National Assessment of Educational Progress (pages 153–162): Neal Thomas
Chapter 15 Propensity Score Estimation with Missing Data (pages 163–174): Ralph B. D'Agostino
Chapter 16 Sensitivity to Nonignorability in Frequentist Inference (pages 175–186): Guoguang Ma and Daniel F. Heitjan
Chapter 17 Statistical Modeling and Computation (pages 187–194): D. Michael Titterington
Chapter 18 Treatment Effects in Before?After Data (pages 195–202): Andrew Gelman
Chapter 19 Multimodality in Mixture Models and Factor Models (pages 203–213): Eric Loken
Chapter 20 Modeling the Covariance and Correlation Matrix of Repeated Measures (pages 215–226): W. John Boscardin and Xiao Zhang
Chapter 21 Robit Regression: A Simple Robust Alternative to Logistic and Probit Regression (pages 227–238): Chuanhai Liu
Chapter 22 Using EM and Data Augmentation for the Competing Risks Model (pages 239–251): Radu V. Craiu and Thierry Duchesne
Chapter 23 Mixed Effects Models and the EM Algorithm (pages 253–264): Florin Vaida, Xiao?Li Meng and Ronghui Xu
Chapter 24 The Sampling/Importance Resampling Algorithm (pages 265–276): Kim?Hung Li
Chapter 25 Whither Applied Bayesian Inference? (pages 277–284): Bradley P. Carlin
Chapter 26 Efficient EM?type Algorithms for Fitting Spectral Lines in High?Energy Astrophysics (pages 285–296): David A. van Dyk and Taeyoung Park
Chapter 27 Improved Predictions of Lynx Trappings Using a Biological Model (pages 297–308): Cavan Reilly and Angelique Zeringue
Chapter 28 Record Linkage Using Finite Mixture Models (pages 309–318): Michael D. Larsen
Chapter 29 Identifying Likely Duplicates by Record Linkage in a Survey of Prostitutes (pages 319–329): Thomas R. Belin, Hemant Ishwaran, Naihua Duan, Sandra H. Berry and David E. Kanouse
Chapter 30 Applying Structural Equation Models with Incomplete Data (pages 331–342): Hal S. Stern and Yoonsook Jeon
Chapter 31 Perceptual Scaling (pages 343–360): Ying Nian Wu, Cheng?En Guo and Song Chun Zhu