頁籤選單縮合
| 題 名 | Semiparametric Penalty Function Method in Partially Linear Model Selection |
|---|---|
| 作 者 | Dong, Chaohua; Gao, Jiti; Tong, Howell; | 書刊名 | Statistica Sinica |
| 卷 期 | 17:1 2007.01[民96.01] |
| 頁 次 | 頁99-114 |
| 分類號 | 319.711 |
| 關鍵詞 | Linear model; Model selection; Nonparametric method; Partially linear model; Semiparametric method; |
| 語 文 | 英文(English) |
| 英文摘要 | Model selection in nonparametric and semiparametric regression is of both theoretical and practical interest. Gao and Tong (2004) proposed a semiparametric leave –more-out cross-validation selection procedure for the choice of both the parametric and nonparametric regressors in nonlinear time series regression model. As recognized by the authors, the implementation of the proposed procedure requires the availability of relatively large sample sizes. In order to address the model selection problem with small or medium sample sizes, we propose a model selection procedure for practical use. By extending the so-called penalty function method proposed in Zheng and Loh(1995, 1997) through the incorporation of features of the leave-one-out cross-validation approach, we develop a semiparametric, consistent selection procedure suitable for the choice of optimum subsets in a partially linear model. The newly proposed method is implemented using the full set of data, and simulations show that is works well for both small and medium sample sizes. |
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