LEADER 05791nam 22006974a 450 001 9910829865903321 005 20210604082827.0 010 $a1-280-26898-0 010 $a9786610268986 010 $a0-470-09045-6 010 $a0-470-09044-8 035 $a(CKB)1000000000377283 035 $a(EBL)219751 035 $a(OCoLC)56717813 035 $a(SSID)ssj0000104656 035 $a(PQKBManifestationID)11130714 035 $a(PQKBTitleCode)TC0000104656 035 $a(PQKBWorkID)10100494 035 $a(PQKB)10143714 035 $a(MiAaPQ)EBC219751 035 $a(iGPub)WILEYB0023891 035 $a(PPN)19687159X 035 $a(EXLCZ)991000000000377283 100 $a20040720d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplied Bayesian modeling and causal inference from incomplete-data perspectives$b[electronic resource] $ean essential journey with Donald Rubin's statistical family /$fedited by Andrew Gelman, Xiao-Li Meng 205 $a1st ed. 210 $aChichester, West Sussex, England ;$aHoboken, NJ $cWiley$dc2004 215 $a1 online resource (437 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-470-09043-X 320 $aIncludes bibliographical references (p. 361-400) and index. 327 $aApplied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives; Contents; Preface; I Casual inference and observational studies; 1 An overview of methods for causal inference from observational studies; 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; 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; 3.1 Introduction 327 $a3.2 Identifying and estimating the average treatment effect3.3 The NSW data; 3.4 Propensity score estimates; 3.5 Conclusions; 4 Medication cost sharing and drug spending in Medicare; 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; 5.1 Experimental sample; 5.2 Constructed observational study; 5.3 Concluding remarks; 6 Fixing broken experiments using the propensity score; 6.1 Introduction; 6.2 The lottery data; 6.3 Estimating the propensity scores; 6.4 Results; 6.5 Concluding remarks 327 $a7 The propensity score with continuous treatments7.1 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; 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; 9.1 Introduction: partially controlled studies; 9.2 Examples of partially controlled studies; 9.3 Principal stratification 327 $a9.4 Estimands9.5 Assumptions; 9.6 Designs and polydesigns; II Missing data modeling; 10 Nonresponse adjustment in government statistical agencies: constraints, inferential goals, and robustness issues; 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; 11.1 Introduction; 11.2 Multiple imputation to achieve comparability of industry and occupation codes 327 $a11.3 Bridging the transition from single-race reporting to multiple-race reporting11.4 Conclusion; 12 Representing the Census undercount by multiple imputation of households; 12.1 Introduction; 12.2 Models; 12.3 Inference; 12.4 Simulation evaluations; 12.5 Conclusion; 13 Statistical disclosure techniques based on multiple imputation; 13.1 Introduction; 13.2 Full synthesis; 13.3 SMIKe and MIKe; 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; 14.1 Introduction 327 $a14.2 Statistical methods in NAEP 330 $aThis book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include:Comprehensive coverage of an imporant area for both research and applications.Adopts a pragmatic approach to describ 410 0$aWiley series in probability and statistics. 606 $aBayesian statistical decision theory 606 $aMissing observations (Statistics) 615 0$aBayesian statistical decision theory. 615 0$aMissing observations (Statistics) 676 $a519.5/42 676 $a519.542 701 $aRubin$b Donald B$0102932 701 $aGelman$b Andrew$044041 701 $aMeng$b Xiao-Li$0970587 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829865903321 996 $aApplied Bayesian modeling and causal inference from incomplete-data perspectives$93928226 997 $aUNINA