کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
417711 | 681560 | 2011 | 15 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Semiparametrically weighted robust estimation of regression models Semiparametrically weighted robust estimation of regression models](/preview/png/417711.png)
A class of two-step robust regression estimators that achieve a high relative efficiency for data from light-tailed, heavy-tailed, and contaminated distributions irrespective of the sample size is proposed and studied. In particular, the least weighted squares (LWS) estimator is combined with data-adaptive weights, which are determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the LWS estimator with the proposed weights preserves robust properties of the initial robust estimate. However, contrary to the existing methods and despite the data-dependent weights, the first-order asymptotic behavior of LWS is fully independent of the initial estimate under mild conditions. Moreover, the proposed estimation method is asymptotically efficient if errors are normally distributed. A simulation study documents these theoretical properties in finite samples; in particular, the relative efficiency of LWS with the proposed weighting schemes can reach 85%–100% in samples of several tens of observations under various distributional models.
Journal: Computational Statistics & Data Analysis - Volume 55, Issue 1, 1 January 2011, Pages 774–788