頁籤選單縮合
| 題 名 | Adaptive Varying-Coefficient Linear Models for Stochastic Processes: Asymptotic Theory |
|---|---|
| 作 者 | Lu, Zudi; Tjøstheim, Dag; Yao, Qiwei; | 書刊名 | Statistica Sinica |
| 卷 期 | 17:1 2007.01[民96.01] |
| 頁 次 | 頁177-197 |
| 分類號 | 319.5 |
| 關鍵詞 | Adaptive varying-coefficient model; Asymptotic normality; β-mixing; Empirical process; Index parameter; Root-n consistency; Uniform convergence; |
| 語 文 | 英文(English) |
| 英文摘要 | We establish the asymptotic theory for the estimation of adaptive varying-coefficient linear models. More specifically, we show that the estimator of the index parameter is rot-n-consistent. It differs from the locally optimal estimator that has been proposed in the literature with a prerequisite that the estimator is within a n-δ –distance of the true value. To this end, we establish two fundamental lemmas for the asymptotic properties of the estimators of parametric components in a general semiparametric setting. Furthermore, the estimation for the coefficient functions is asymptotically adaptive to the unknown index parameter. Asymptotic properties are derived using the empirical process theory for strictly stationary β-processes. |
本系統中英文摘要資訊取自各篇刊載內容。