Generalized Linear Models for Small-Area Estimation Export

@article{ghosh:nata:stroud:carlin:1998, abstract = {Bayesian methods have been used quite extensively in recent years for solving small-area estimation problems. Particularly effective in this regard has been the hierarchical or empirical Bayes approach, which is especially suitable for a systematic connection of local areas through models. However, the development to date has mainly concentrated on continuous-valued variates. Often the survey data are discrete or categorical, so that hierarchical or empirical Bayes techniques designed for continuous variates are inappropriate. This article considers hierarchical Bayes generalized linear models for a unified analysis of both discrete and continuous data. A general theorem is provided that ensures the propriety of posteriors under diffuse priors. This result is then extended to the case of spatial generalized linear models. The hierarchical Bayes procedure is implemented via Markov chain Monte Carlo integration techniques. Two examples (one featuring spatial correlation structure) are given to illustrate the general method.}, author = {Ghosh, Malay and Natarajan, Kannan and Stroud, T. W. F. and Carlin, Bradley P.}, citeulike-article-id = {943057}, citeulike-linkout-0 = {http://www.jstor.org/stable/2669623}, journal = {Journal of the American Statistical Association}, keywords = {bayesian, glm, mixed\_model, sampling, small\_area, stat9100svy, survey}, number = {441}, pages = {273--282}, posted-at = {2006-11-14 15:37:52}, priority = {2}, title = {Generalized Linear Models for Small-Area Estimation}, url = {http://www.jstor.org/stable/2669623}, volume = {93}, year = {1998} }

TY - JOUR ID - ghosh:nata:stroud:carlin:1998 L3 - citeulike-article-id:943057 N2 - Bayesian methods have been used quite extensively in recent years for solving small-area estimation problems. Particularly effective in this regard has been the hierarchical or empirical Bayes approach, which is especially suitable for a systematic connection of local areas through models. However, the development to date has mainly concentrated on continuous-valued variates. Often the survey data are discrete or categorical, so that hierarchical or empirical Bayes techniques designed for continuous variates are inappropriate. This article considers hierarchical Bayes generalized linear models for a unified analysis of both discrete and continuous data. A general theorem is provided that ensures the propriety of posteriors under diffuse priors. This result is then extended to the case of spatial generalized linear models. The hierarchical Bayes procedure is implemented via Markov chain Monte Carlo integration techniques. Two examples (one featuring spatial correlation structure) are given to illustrate the general method. IS - 441 JF - Journal of the American Statistical Association EP - 282 TI - Generalized Linear Models for Small-Area Estimation VL - 93 SP - 273 KW - bayesian KW - glm KW - mixed_model KW - sampling KW - small_area KW - stat9100svy KW - survey AU - Ghosh, Malay AU - Natarajan, Kannan AU - Stroud, TWF AU - Carlin, Bradley PY - 1998/// UR - http://www.jstor.org/stable/2669623 ER -