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Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied 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
3.2 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 7 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 9.4 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 11.3 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 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910144711403321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives [[electronic resource] ] : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina |
519.5/42
519.542 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied 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
3.2 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 7 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 9.4 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 11.3 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 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910829865903321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng
| Applied Bayesian modeling and causal inference from incomplete-data perspectives : an essential journey with Donald Rubin's statistical family / / edited by Andrew Gelman, Xiao-Li Meng |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 |
| Descrizione fisica | 1 online resource (437 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) |
RubinDonald B
GelmanAndrew MengXiao-Li |
| Collana | Wiley series in probability and statistics |
| Soggetto topico |
Bayesian statistical decision theory
Missing observations (Statistics) |
| ISBN |
1-280-26898-0
9786610268986 0-470-09045-6 0-470-09044-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Applied 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
3.2 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 7 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 9.4 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 11.3 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 14.2 Statistical methods in NAEP |
| Record Nr. | UNINA-9910876584003321 |
| Chichester, West Sussex, England ; ; Hoboken, NJ, : Wiley, c2004 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Red state, blue state, rich state, poor state [[electronic resource] ] : why Americans vote the way they do / / Andrew Gelman ... [et al.]
| Red state, blue state, rich state, poor state [[electronic resource] ] : why Americans vote the way they do / / Andrew Gelman ... [et al.] |
| Pubbl/distr/stampa | Princeton, : Princeton University Press, c2008 |
| Descrizione fisica | 1 online resource (240 p.) |
| Disciplina | 324.973 |
| Altri autori (Persone) | GelmanAndrew |
| Soggetto topico |
Elections - United States
Political parties - United States Politics, Practical - United States Presidents - Election |
| ISBN |
1-282-08698-7
1-282-45832-9 9786612458323 9786612086984 1-4008-2836-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Red state, blue state -- Rich state, poor state -- Income and voting over time -- Religious reds and secular blues -- Polarization -- The Northeast is like Europe, the South is like Mexico -- Politicians -- How the talking heads can be so confused -- Putting it all together. |
| Record Nr. | UNINA-9910345156803321 |
| Princeton, : Princeton University Press, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||