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題 名 | Adaptive Bayesian Criteria in Variable Selection for Generalized Linear Models |
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作 者 | Wang, Xinlei; George, Edward I.; | 書刊名 | Statistica Sinica |
卷 期 | 17:2 2007.04[民96.04] |
頁 次 | 頁667-690 |
分類號 | 319.711 |
關鍵詞 | AIC; BIC; Empirical Bayes; Fully Bayes; Hierarchical Bayes; Laplace approximation; |
語 文 | 英文(English) |
英文摘要 | For the problem of variable selection in generalized linear models. We develop various adaptive Bayesian criteria. Using a hierarchical mixture setup for model uncertainty, combined with an integrated Laplace approximation, we derive Empirical Bayes and Fully Bayes criteria that can be computed easily and quickly. The performance of these criteria is assessed via simulation and compared to other criteria such as AIC and BIC on normal, logistic and Poisson regression model classes. A Fully Bayes Criterion based on a restricted region hyperprior seems to be the most promising. Finally, our criteria are illustrated and compared with competitors on a data example. |
本系統中英文摘要資訊取自各篇刊載內容。