{"product_id":"applied-bayesian-modeling-and-causal-inference-from-incomplete-data-perspectives-hardback-9780470090435","title":"Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (Hardback) 9780470090435","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eApplied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAndrew Gelman (Edited by), Xiao-Li Meng (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470090435, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 23 July 2004\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e440 pages\u003cbr\u003e23.6 x 15.9 x 2.8 cm, 0.794 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"I congratulate the editors on this volume; it really is an essential and very enjoyable journey with Don Rubin's statistical family.\" (\u003ci\u003eBiometrics\u003c\/i\u003e, September 2006)  \u003cp\u003e\"…contains much current important work…\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, November 2005)\u003c\/p\u003e \u003cp\u003e\"This a useful reference book on an important topic with applications to a wide range of disciplines.\" (\u003ci\u003eCHOICE\u003c\/i\u003e, September 2005)\u003c\/p\u003e \u003cp\u003e“With this variety of papers, the reader is bound to find some papers interesting…” (\u003ci\u003eJournal of Applied Statistics\u003c\/i\u003e, Vol.32, No.3, April 2005)\u003c\/p\u003e \u003cp\u003e“I strongly recommend that libraries have a copy of this book in their reference section.” (\u003ci\u003eJournal of the Royal Statistical Society Series A\u003c\/i\u003e, June 2005)\u003c\/p\u003e \u003cp\u003e\"...a very useful addition to academic libraries…\" (\u003ci\u003eShort Book Reviews\u003c\/i\u003e, Vol.24, No.3, December 2004)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eApplied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin's Statistical Family\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cp\u003eKey features of the book include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eComprehensive coverage of an imporant area for both research and applications.\u003c\/li\u003e \u003cli\u003eAdopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques.\u003c\/li\u003e \u003cli\u003eCovers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference.\u003c\/li\u003e \u003cli\u003eIncludes a number of applications from the social and health sciences.\u003c\/li\u003e \u003cli\u003eEdited and authored by highly respected researchers in the area.\u003c\/li\u003e \u003c\/ul\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI Casual inference and observational studies 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An overview of methods for causal inference from observational studies, by Sander Greenland 3\u003c\/b\u003e\u003cbr\u003e1.1 Introduction 3\u003cbr\u003e1.2 Approaches based on causal models 3\u003cbr\u003e1.3 Canonical inference 9\u003cbr\u003e1.4 Methodologic modeling 10\u003cbr\u003e1.5 Conclusion 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Matching in observational studies, by Paul R. Rosenbaum 15\u003c\/b\u003e\u003cbr\u003e2.1 The role of matching in observational studies 15\u003cbr\u003e2.2 Why match? 16\u003cbr\u003e2.3 Two key issues: balance and structure 17\u003cbr\u003e2.4 Additional issues 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia 25\u003c\/b\u003e\u003cbr\u003e3.1 Introduction 25\u003cbr\u003e3.2 Identifying and estimating the average treatment effect 27\u003cbr\u003e3.3 The NSWdata 29\u003cbr\u003e3.4 Propensity score estimates 31\u003cbr\u003e3.5 Conclusions 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams 37\u003c\/b\u003e\u003cbr\u003e4.1 Methods 38\u003cbr\u003e4.2 Results 40\u003cbr\u003e4.3 Study limitations 45\u003cbr\u003e4.4 Conclusions and policy implications 46\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A comparison of experimental and observational data analyses, by Jennifer L. Hill, Jerome P. Reiter, and Elaine L. Zanutto 49\u003c\/b\u003e\u003cbr\u003e5.1 Experimental sample 50\u003cbr\u003e5.2 Constructed observational study 51\u003cbr\u003e5.3 Concluding remarks 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fixing broken experiments using the propensity score, by Bruce Sacerdote 61\u003c\/b\u003e\u003cbr\u003e6.1 Introduction 61\u003cbr\u003e6.2 The lottery data 62\u003cbr\u003e6.3 Estimating the propensity scores 63\u003cbr\u003e6.4 Results 65\u003cbr\u003e6.5 Concluding remarks 71\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens 73\u003c\/b\u003e\u003cbr\u003e7.1 Introduction 73\u003cbr\u003e7.2 The basic framework 74\u003cbr\u003e7.3 Bias removal using the GPS 76\u003cbr\u003e7.4 Estimation and inference 78\u003cbr\u003e7.5 Application: the Imbens–Rubin–Sacerdote lottery sample 79\u003cbr\u003e7.6 Conclusion 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Causal inference with instrumental variables, by Junni L. Zhang 85\u003c\/b\u003e\u003cbr\u003e8.1 Introduction 85\u003cbr\u003e8.2 Key assumptions for the LATE interpretation of the IV estimand 87\u003cbr\u003e8.3 Estimating causal effects with IV 90\u003cbr\u003e8.4 Some recent applications 95\u003cbr\u003e8.5 Discussion 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Principal stratification, by Constantine E. Frangakis 97\u003c\/b\u003e\u003cbr\u003e9.1 Introduction: partially controlled studies 97\u003cbr\u003e9.2 Examples of partially controlled studies 97\u003cbr\u003e9.3 Principal stratification 101\u003cbr\u003e9.4 Estimands 102\u003cbr\u003e9.5 Assumptions 104\u003cbr\u003e9.6 Designs and polydesigns 107\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Missing data modeling 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues, by John L. Eltinge 111\u003c\/b\u003e\u003cbr\u003e10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies 111\u003cbr\u003e10.2 Constraints 112\u003cbr\u003e10.3 Complex estimand structures, inferential goals, and utility functions 112\u003cbr\u003e10.4 Robustness 113\u003cbr\u003e10.5 Closing remarks 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bridging across changes in classification systems, by Nathaniel Schenker 117\u003c\/b\u003e\u003cbr\u003e11.1 Introduction 117\u003cbr\u003e11.2 Multiple imputation to achieve comparability of industry and occupation codes 118\u003cbr\u003e11.3 Bridging the transition from single-race reporting to multiple-race reporting 123\u003cbr\u003e11.4 Conclusion 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky 129\u003c\/b\u003e\u003cbr\u003e12.1 Introduction 129\u003cbr\u003e12.2 Models 131\u003cbr\u003e12.3 Inference 134\u003cbr\u003e12.4 Simulation evaluations 138\u003cbr\u003e12.5 Conclusion 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eE. Raghunathan 141\u003c\/b\u003e\u003cbr\u003e13.1 Introduction 141\u003cbr\u003e13.2 Full synthesis 143\u003cbr\u003e13.3 SMIKe andMIKe 144\u003cbr\u003e13.4 Analysis of synthetic samples 147\u003cbr\u003e13.5 An application 149\u003cbr\u003e13.6 Conclusions 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas 153\u003c\/b\u003e\u003cbr\u003e14.1 Introduction 153\u003cbr\u003e14.2 Statistical methods in NAEP 155\u003cbr\u003e14.3 Split and balanced designs for estimating population parameters 157\u003cbr\u003e14.4 Maximum likelihood estimation 159\u003cbr\u003e14.5 The role of secondary covariates 160\u003cbr\u003e14.6 Conclusions 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Propensity score estimation with missing data, by Ralph B. D'Agostino Jr. 163\u003c\/b\u003e\u003cbr\u003e15.1 Introduction 163\u003cbr\u003e15.2 Notation 165\u003cbr\u003e15.3 Applied example:March of Dimes data 168\u003cbr\u003e15.4 Conclusion and future directions 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan 175\u003c\/b\u003e\u003cbr\u003e16.1 Missing data in clinical trials 175\u003cbr\u003e16.2 Ignorability and bias 175\u003cbr\u003e16.3 A nonignorable selection model 176\u003cbr\u003e16.4 Sensitivity of the mean and variance 177\u003cbr\u003e16.5 Sensitivity of the power 178\u003cbr\u003e16.6 Sensitivity of the coverage probability 180\u003cbr\u003e16.7 An example 184\u003cbr\u003e16.8 Discussion 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Statistical modeling and computation 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Statistical modeling and computation, by D. Michael Titterington 189\u003c\/b\u003e\u003cbr\u003e17.1 Regression models 190\u003cbr\u003e17.2 Latent-variable problems 191\u003cbr\u003e17.3 Computation: non-Bayesian 191\u003cbr\u003e17.4 Computation: Bayesian 192\u003cbr\u003e17.5 Prospects for the future 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Treatment effects in before-after data, by Andrew Gelman 195\u003c\/b\u003e\u003cbr\u003e18.1 Default statistical models of treatment effects 195\u003cbr\u003e18.2 Before-after correlation is typically larger for controls than for treated units 196\u003cbr\u003e18.3 A class of models for varying treatment effects 200\u003cbr\u003e18.4 Discussion 201\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Multimodality in mixture models and factor models, by Eric Loken 203\u003c\/b\u003e\u003cbr\u003e19.1 Multimodality in mixture models 204\u003cbr\u003e19.2 Multimodal posterior distributions in continuous latent variable models 209\u003cbr\u003e19.3 Summary 212\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Modeling the covariance and correlation matrix of repeated measures, by W. John Boscardin and Xiao Zhang 215\u003c\/b\u003e\u003cbr\u003e20.1 Introduction 215\u003cbr\u003e20.2 Modeling the covariance matrix 216\u003cbr\u003e20.3 Modeling the correlation matrix 218\u003cbr\u003e20.4 Modeling a mixed covariance-correlation matrix 220\u003cbr\u003e20.5 Nonzero means and unbalanced data 220\u003cbr\u003e20.6 Multivariate probit model 221\u003cbr\u003e20.7 Example: covariance modeling 222\u003cbr\u003e20.8 Example: mixed data 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Robit regression: a simple robust alternative to logistic and probit regression, by Chuanhai Liu 227\u003c\/b\u003e\u003cbr\u003e21.1 Introduction 227\u003cbr\u003e21.2 The robit model 228\u003cbr\u003e21.3 Robustness of likelihood-based inference using logistic, probit, and robit regression models 230\u003cbr\u003e21.4 Complete data for simple maximum likelihood estimation 231\u003cbr\u003e21.5 Maximum likelihood estimation using EM-type algorithms 233\u003cbr\u003e21.6 A numerical example 235\u003cbr\u003e21.7 Conclusion 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Using EM and data augmentation for the competing risks model, by Radu V. Craiu and Thierry Duchesne 239\u003c\/b\u003e\u003cbr\u003e22.1 Introduction 239\u003cbr\u003e22.2 The model 240\u003cbr\u003e22.3 EM-based analysis 243\u003cbr\u003e22.4 Bayesian analysis 244\u003cbr\u003e22.5 Example 248\u003cbr\u003e22.6 Discussion and further work 250\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Mixed effects models and the EM algorithm, by Florin Vaida, Xiao-Li Meng, and Ronghui Xu 253\u003c\/b\u003e\u003cbr\u003e23.1 Introduction 253\u003cbr\u003e23.2 Binary regression with random effects 254\u003cbr\u003e23.3 Proportional hazards mixed-effects models 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 The sampling\/importance resampling algorithm, by Kim-Hung Li 265\u003c\/b\u003e\u003cbr\u003e24.1 Introduction 265\u003cbr\u003e24.2 SIR algorithm 266\u003cbr\u003e24.3 Selection of the pool size 267\u003cbr\u003e24.4 Selection criterion of the importance sampling distribution 271\u003cbr\u003e24.5 The resampling algorithms 272\u003cbr\u003e24.6 Discussion 276\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Applied Bayesian inference 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Whither applied Bayesian inference?, by Bradley P. Carlin 279\u003c\/b\u003e\u003cbr\u003e25.1 Where we've been 279\u003cbr\u003e25.2 Where we are 281\u003cbr\u003e25.3 Where we're going 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Efficient EM-type algorithms for fitting spectral lines in high-energy astrophysics, by David A. van Dyk and Taeyoung Park 285\u003c\/b\u003e\u003cbr\u003e26.1 Application-specific statistical methods 285\u003cbr\u003e26.2 The Chandra X-ray observatory 287\u003cbr\u003e26.3 Fitting narrow emission lines 289\u003cbr\u003e26.4 Model checking and model selection 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Improved predictions of lynx trappings using a biological model, by Cavan Reilly and Angelique Zeringue 297\u003c\/b\u003e\u003cbr\u003e27.1 Introduction 297\u003cbr\u003e27.2 The current best model 298\u003cbr\u003e27.3 Biological models for predator prey systems 299\u003cbr\u003e27.4 Some statistical models based on the Lotka-Volterra system 300\u003cbr\u003e27.5 Computational aspects of posterior inference 302\u003cbr\u003e27.6 Posterior predictive checks and model expansion 304\u003cbr\u003e27.7 Prediction with the posterior mode 307\u003cbr\u003e27.8 Discussion 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Record linkage using finite mixture models, by Michael D. Larsen 309\u003c\/b\u003e\u003cbr\u003e28.1 Introduction to record linkage 309\u003cbr\u003e28.2 Record linkage 310\u003cbr\u003e28.3 Mixture models 311\u003cbr\u003e28.4 Application 314\u003cbr\u003e28.5 Analysis of linked files 316\u003cbr\u003e28.6 Bayesian hierarchical record linkage 317\u003cbr\u003e28.7 Summary 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 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 319\u003c\/b\u003e\u003cbr\u003e29.1 Concern about duplicates in an anonymous survey 319\u003cbr\u003e29.2 General frameworks for record linkage 321\u003cbr\u003e29.3 Estimating probabilities of duplication in the Los Angeles Women's Health Risk Study 322\u003cbr\u003e29.4 Discussion 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Applying structural equation models with incomplete data, by Hal S. Stern and Yoonsook Jeon 331\u003c\/b\u003e\u003cbr\u003e30.1 Structural equation models 332\u003cbr\u003e30.2 Bayesian inference for structural equation models 334\u003cbr\u003e30.3 Iowa Youth and Families Project example 339\u003cbr\u003e30.4 Summary and discussion 342\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo, and Song Chun Zhu 343\u003c\/b\u003e\u003cbr\u003e31.1 Introduction 343\u003cbr\u003e31.2 Sparsity and minimax entropy 347\u003cbr\u003e31.3 Complexity scaling law. 353\u003cbr\u003e31.4 Perceptibility scaling law 356\u003cbr\u003e31.5 Texture = imperceptible structures 358\u003cbr\u003e31.6 Perceptibility and sparsity 359\u003c\/p\u003e \u003cp\u003eReferences 361\u003cbr\u003eIndex 401\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52501164097816,"sku":"9780470090435","price":74.77,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470090435.jpg?v=1786213830","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/applied-bayesian-modeling-and-causal-inference-from-incomplete-data-perspectives-hardback-9780470090435","provider":"Freshly Printed Books","version":"1.0","type":"link"}