標題: | 具有AR(1)誤差的迴歸模型的線性修正平均估計值 Linear Trimmed Means for the Linear Regression with AR(1) Errors Model |
作者: | 彭豐洋 Feng-Yang Peng 陳鄰安 Lin-An Chen 統計學研究所 |
關鍵字: | 高斯馬可夫定理;廣義最小平方法估計量;線性修正平均數;穩健性估計;Gauss Markov theorem;Gnenralized least squares estimator;linear trimmed mean;robust estimator |
公開日期: | 2004 |
摘要: | 延續Lai (2003)其具有AR(1)誤差的線性迴歸模型的穩健性估計基本架構,我們證明了在大樣本的情形下廣義修正平均值估計量能夠有類似 Gauss Markov Theorem 的性質。我們稱其為穩健型態的 Gauss Markov Theorem。
我們進而利用模擬的方法以及實例的分析,說明該估計量的特性與效率。 For the linear regression with AR(1) errors model, a robust type generalized and feasible generalized estimators of Lai et al. (2003) of regression parameters are shown to have the desired property of robust type Gauss Markov theorem. It is done by shown that these two estimators are, respectively, the best among classes of linear trimmed means. Monte Carlo and data analysis for this technique have been performed. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009226517 http://hdl.handle.net/11536/76888 |
Appears in Collections: | Thesis |
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