頁籤選單縮合
| 題 名 | Strong Limit Theorems on Model Selection in Generalized Linear Regression with Binomial Responses |
|---|---|
| 作 者 | Qian, Guoqi; Wu, Yuehua; | 書刊名 | Statistica Sinica |
| 卷 期 | 16:4 民95.10 |
| 頁 次 | 頁1335-1365 |
| 分類號 | 319.711 |
| 關鍵詞 | Generalized linear models; Law of the iterated logarithm; Maximum likelihood estimator; Model selection; Strong consistency; |
| 語 文 | 英文(English) |
| 英文摘要 | We prove a law of iterated logarithm for the maximum likelihood estimator of the parameters in a generalized linear regression model with binomial response. This result is then used to derive an asymptotic bound for the difference between the maximum log-likelihood function and the true log-likelihood. It is further used to establish the strong consistency of some penalized likelihood based model selection criteria. We have shown that, under some general conditions, a model selection criterion will select the simplest correct model almost surely if the penalty term is an increasing function of the model dimension and has an order between O(log log n) and O(n). Cases involving the commonly used link functions are discussed for illustration of the results. |
本系統中英文摘要資訊取自各篇刊載內容。