{"product_id":"applied-bayesian-modeling-and-causal-inference-from-incomplete-data-perspectives-isbn-9780470090435","title":"Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives","description":"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.  \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  Preface.  \u003cp\u003e\u003cb\u003eI Casual inference and observational studies.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 An overview of methods for causal inference from observational studies, by Sander Greenland.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Approaches based on causal models.\u003c\/p\u003e \u003cp\u003e1.3 Canonical inference.\u003c\/p\u003e \u003cp\u003e1.4 Methodologic modeling.\u003c\/p\u003e \u003cp\u003e1.5 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Matching in observational studies, by Paul R. Rosenbaum.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The role of matching in observational studies.\u003c\/p\u003e \u003cp\u003e2.2 Why match?\u003c\/p\u003e \u003cp\u003e2.3 Two key issues: balance and structure.\u003c\/p\u003e \u003cp\u003e2.4 Additional issues.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Estimating causal effects in nonexperimental studies, by Rajeev Dehejia.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Identifying and estimating the average treatment effect.\u003c\/p\u003e \u003cp\u003e3.3 The NSWdata.\u003c\/p\u003e \u003cp\u003e3.4 Propensity score estimates.\u003c\/p\u003e \u003cp\u003e3.5 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Medication cost sharing and drug spending in Medicare, by Alyce S. Adams.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Methods.\u003c\/p\u003e \u003cp\u003e4.2 Results.\u003c\/p\u003e \u003cp\u003e4.3 Study limitations.\u003c\/p\u003e \u003cp\u003e4.4 Conclusions and policy implications.\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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Experimental sample.\u003c\/p\u003e \u003cp\u003e5.2 Constructed observational study.\u003c\/p\u003e \u003cp\u003e5.3 Concluding remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fixing broken experiments using the propensity score, by Bruce Sacerdote.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 The lottery data.\u003c\/p\u003e \u003cp\u003e6.3 Estimating the propensity scores.\u003c\/p\u003e \u003cp\u003e6.4 Results.\u003c\/p\u003e \u003cp\u003e6.5 Concluding remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 The propensity score with continuous treatments, by Keisuke Hirano and Guido W. Imbens.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 The basic framework.\u003c\/p\u003e \u003cp\u003e7.3 Bias removal using the GPS.\u003c\/p\u003e \u003cp\u003e7.4 Estimation and inference.\u003c\/p\u003e \u003cp\u003e7.5 Application: the Imbens–Rubin–Sacerdote lottery sample.\u003c\/p\u003e \u003cp\u003e7.6 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Causal inference with instrumental variables, by Junni L. Zhang.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Key assumptions for the LATE interpretation of the IV estimand.\u003c\/p\u003e \u003cp\u003e8.3 Estimating causal effects with IV.\u003c\/p\u003e \u003cp\u003e8.4 Some recent applications.\u003c\/p\u003e \u003cp\u003e8.5 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Principal stratification, by Constantine E. Frangakis.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction: partially controlled studies.\u003c\/p\u003e \u003cp\u003e9.2 Examples of partially controlled studies.\u003c\/p\u003e \u003cp\u003e9.3 Principal stratification.\u003c\/p\u003e \u003cp\u003e9.4 Estimands.\u003c\/p\u003e \u003cp\u003e9.5 Assumptions.\u003c\/p\u003e \u003cp\u003e9.6 Designs and polydesigns.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII Missing data modeling.\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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction: a wide spectrum of nonresponse adjustment efforts in government statistical agencies.\u003c\/p\u003e \u003cp\u003e10.2 Constraints.\u003c\/p\u003e \u003cp\u003e10.3 Complex estimand structures, inferential goals, and utility functions.\u003c\/p\u003e \u003cp\u003e10.4 Robustness.\u003c\/p\u003e \u003cp\u003e10.5 Closing remarks.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Bridging across changes in classification systems, by Nathaniel Schenker.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Multiple imputation to achieve comparability of industry and occupation codes.\u003c\/p\u003e \u003cp\u003e11.3 Bridging the transition from single-race reporting to multiple-race reporting.\u003c\/p\u003e \u003cp\u003e11.4 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Representing the Census undercount by multiple imputation of households, by Alan M. Zaslavsky.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction.\u003c\/p\u003e \u003cp\u003e12.2 Models.\u003c\/p\u003e \u003cp\u003e12.3 Inference.\u003c\/p\u003e \u003cp\u003e12.4 Simulation evaluations.\u003c\/p\u003e \u003cp\u003e12.5 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Statistical disclosure techniques based on multiple imputation, by Roderick J. A. Little, Fang Liu, and Trivellore E. Raghunathan.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Full synthesis.\u003c\/p\u003e \u003cp\u003e13.3 SMIKe andMIKe.\u003c\/p\u003e \u003cp\u003e13.4 Analysis of synthetic samples.\u003c\/p\u003e \u003cp\u003e13.5 An application.\u003c\/p\u003e \u003cp\u003e13.6 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Designs producing balanced missing data: examples from the National Assessment of Educational Progress, by Neal Thomas.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 Statistical methods in NAEP.\u003c\/p\u003e \u003cp\u003e14.3 Split and balanced designs for estimating population parameters.\u003c\/p\u003e \u003cp\u003e14.4 Maximum likelihood estimation.\u003c\/p\u003e \u003cp\u003e14.5 The role of secondary covariates.\u003c\/p\u003e \u003cp\u003e14.6 Conclusions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Propensity score estimation with missing data, by Ralph B. D’Agostino Jr.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 Notation.\u003c\/p\u003e \u003cp\u003e15.3 Applied example:March of Dimes data.\u003c\/p\u003e \u003cp\u003e15.4 Conclusion and future directions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Sensitivity to nonignorability in frequentist inference, by Guoguang Ma and Daniel F. Heitjan.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Missing data in clinical trials.\u003c\/p\u003e \u003cp\u003e16.2 Ignorability and bias.\u003c\/p\u003e \u003cp\u003e16.3 A nonignorable selection model.\u003c\/p\u003e \u003cp\u003e16.4 Sensitivity of the mean and variance.\u003c\/p\u003e \u003cp\u003e16.5 Sensitivity of the power.\u003c\/p\u003e \u003cp\u003e16.6 Sensitivity of the coverage probability.\u003c\/p\u003e \u003cp\u003e16.7 An example.\u003c\/p\u003e \u003cp\u003e16.8 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII Statistical modeling and computation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Statistical modeling and computation, by D. Michael Titterington.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Regression models.\u003c\/p\u003e \u003cp\u003e17.2 Latent-variable problems.\u003c\/p\u003e \u003cp\u003e17.3 Computation: non-Bayesian.\u003c\/p\u003e \u003cp\u003e17.4 Computation: Bayesian.\u003c\/p\u003e \u003cp\u003e17.5 Prospects for the future.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Treatment effects in before-after data, by Andrew Gelman.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Default statistical models of treatment effects.\u003c\/p\u003e \u003cp\u003e18.2 Before-after correlation is typically larger for controls than for treated units.\u003c\/p\u003e \u003cp\u003e18.3 A class of models for varying treatment effects.\u003c\/p\u003e \u003cp\u003e18.4 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Multimodality in mixture models and factor models, by Eric Loken.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Multimodality in mixture models.\u003c\/p\u003e \u003cp\u003e19.2 Multimodal posterior distributions in continuous latent variable models.\u003c\/p\u003e \u003cp\u003e19.3 Summary.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Modeling the covariance and correlation matrix of repeated measures, by W. John Boscardin and Xiao Zhang.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction.\u003c\/p\u003e \u003cp\u003e20.2 Modeling the covariance matrix.\u003c\/p\u003e \u003cp\u003e20.3 Modeling the correlation matrix.\u003c\/p\u003e \u003cp\u003e20.4 Modeling a mixed covariance-correlation matrix.\u003c\/p\u003e \u003cp\u003e20.5 Nonzero means and unbalanced data.\u003c\/p\u003e \u003cp\u003e20.6 Multivariate probit model.\u003c\/p\u003e \u003cp\u003e20.7 Example: covariance modeling.\u003c\/p\u003e \u003cp\u003e20.8 Example: mixed data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Robit regression: a simple robust alternative to logistic and probit regression, by Chuanhai Liu.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction.\u003c\/p\u003e \u003cp\u003e21.2 The robit model.\u003c\/p\u003e \u003cp\u003e21.3 Robustness of likelihood-based inference using logistic, probit, and robit regression models.\u003c\/p\u003e \u003cp\u003e21.4 Complete data for simple maximum likelihood estimation.\u003c\/p\u003e \u003cp\u003e21.5 Maximum likelihood estimation using EM-type algorithms.\u003c\/p\u003e \u003cp\u003e21.6 A numerical example.\u003c\/p\u003e \u003cp\u003e21.7 Conclusion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Using EM and data augmentation for the competing risks model, by Radu V. Craiu and Thierry Duchesne.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction.\u003c\/p\u003e \u003cp\u003e22.2 The model.\u003c\/p\u003e \u003cp\u003e22.3 EM-based analysis.\u003c\/p\u003e \u003cp\u003e22.4 Bayesian analysis.\u003c\/p\u003e \u003cp\u003e22.5 Example.\u003c\/p\u003e \u003cp\u003e22.6 Discussion and further work.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Mixed effects models and the EM algorithm, by Florin Vaida, Xiao-Li Meng, and Ronghui Xu.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction.\u003c\/p\u003e \u003cp\u003e23.2 Binary regression with random effects.\u003c\/p\u003e \u003cp\u003e23.3 Proportional hazards mixed-effects models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 The sampling\/importance resampling algorithm, by Kim-Hung Li.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction.\u003c\/p\u003e \u003cp\u003e24.2 SIR algorithm.\u003c\/p\u003e \u003cp\u003e24.3 Selection of the pool size.\u003c\/p\u003e \u003cp\u003e24.4 Selection criterion of the importance sampling distribution.\u003c\/p\u003e \u003cp\u003e24.5 The resampling algorithms.\u003c\/p\u003e \u003cp\u003e24.6 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV Applied Bayesian inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Whither applied Bayesian inference?, by Bradley P. Carlin.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Where we’ve been.\u003c\/p\u003e \u003cp\u003e25.2 Where we are.\u003c\/p\u003e \u003cp\u003e25.3 Where we’re going.\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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Application-specific statistical methods .\u003c\/p\u003e \u003cp\u003e26.2 The Chandra X-ray observatory.\u003c\/p\u003e \u003cp\u003e26.3 Fitting narrow emission lines.\u003c\/p\u003e \u003cp\u003e26.4 Model checking and model selection.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Improved predictions of lynx trappings using a biological model, by Cavan Reilly and Angelique Zeringue.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction.\u003c\/p\u003e \u003cp\u003e27.2 The current best model.\u003c\/p\u003e \u003cp\u003e27.3 Biological models for predator prey systems.\u003c\/p\u003e \u003cp\u003e27.4 Some statistical models based on the Lotka-Volterra system.\u003c\/p\u003e \u003cp\u003e27.5 Computational aspects of posterior inference.\u003c\/p\u003e \u003cp\u003e27.6 Posterior predictive checks and model expansion.\u003c\/p\u003e \u003cp\u003e27.7 Prediction with the posterior mode.\u003c\/p\u003e \u003cp\u003e27.8 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Record linkage using finite mixture models, by Michael D. Larsen.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Introduction to record linkage.\u003c\/p\u003e \u003cp\u003e28.2 Record linkage.\u003c\/p\u003e \u003cp\u003e28.3 Mixture models.\u003c\/p\u003e \u003cp\u003e28.4 Application.\u003c\/p\u003e \u003cp\u003e28.5 Analysis of linked files.\u003c\/p\u003e \u003cp\u003e28.6 Bayesian hierarchical record linkage.\u003c\/p\u003e \u003cp\u003e28.7 Summary.\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.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Concern about duplicates in an anonymous survey.\u003c\/p\u003e \u003cp\u003e29.2 General frameworks for record linkage.\u003c\/p\u003e \u003cp\u003e29.3 Estimating probabilities of duplication in the Los Angeles Women’s Health Risk Study.\u003c\/p\u003e \u003cp\u003e29.4 Discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Applying structural equation models with incomplete data, by Hal S. Stern and Yoonsook Jeon.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Structural equation models.\u003c\/p\u003e \u003cp\u003e30.2 Bayesian inference for structural equation models.\u003c\/p\u003e \u003cp\u003e30.3 Iowa Youth and Families Project example.\u003c\/p\u003e \u003cp\u003e30.4 Summary and discussion.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Perceptual scaling, by Ying Nian Wu, Cheng-En Guo, and Song Chun Zhu.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction.\u003c\/p\u003e \u003cp\u003e31.2 Sparsity and minimax entropy.\u003c\/p\u003e \u003cp\u003e31.3 Complexity scaling law.\u003c\/p\u003e \u003cp\u003e31.4 Perceptibility scaling law.\u003c\/p\u003e \u003cp\u003e31.5 Texture = imperceptible structures.\u003c\/p\u003e \u003cp\u003e31.6 Perceptibility and sparsity.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\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 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).  Statistical techniques that take account of missing data in a clinical trial, census, or other experiments, observational studies, and surveys are of increasing importance. The use of increasingly powerful computers and algorithms has made it possible to study statistical problems from a Bayesian perspective. These topics are highly active research areas and have important applications across a wide range of disciplines.  \u003cp\u003eThis book is a collection of articles from leading researchers on statistical methods relating to missing data analysis, causal inference, and statistical modeling, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. The book is dedicated to Professor Donald Rubin, on the occasion of his 60th birthday, in recognition of his many and wide-ranging contributions to statistics, particularly to the topic of statistical analysis with missing data.\u003c\/p\u003e \u003cdiv\u003e \u003cul\u003e \u003cli\u003eProvides an authoritative overview of several important statistical topics 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 range of applications from the social, health, biological, and physical sciences.\u003c\/li\u003e \u003cli\u003eFeatures overview chapters for each part of the book.\u003c\/li\u003e \u003cli\u003eEdited and authored by highly respected researchers in the area.\u003c\/li\u003e \u003c\/ul\u003e \u003c\/div\u003e \u003cp\u003e\u003ci\u003eApplied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives\u003c\/i\u003e presents an overview with examples of these key topics suitable for researchers in all areas of statistics. It adopts a practical approach suitable for applied statisticians working in social and political sciences, biological and medical sciences, and physical sciences, as well as graduate students of statistics and biostatistics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988749566181,"sku":"NP9780470090435","price":158.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470090435.jpg?v=1761781442","url":"https:\/\/k12savings.com\/es\/products\/applied-bayesian-modeling-and-causal-inference-from-incomplete-data-perspectives-isbn-9780470090435","provider":"K12savings","version":"1.0","type":"link"}