標題: An efficient class of weighted trimmed means for linear regression models
作者: Chen, LA
統計學研究所
Institute of Statistics
關鍵字: initial estimator;symmetric quantile;weighted trimmed mean
公開日期: 1-七月-1997
摘要: We propose and study a class of weighted trimmed means based on the symmetric quantile functions for the location and linear regression models. A robustness comparison with the underlying distribution of a symmetric-type heavy tail is given. The weighted trimmed mean in optimal trimming under symmetric distributions is shown to have an asymptotic variance very close to the Cramer-Rao lower bound. For fixed weight setting, the weighted trimmed mean is still relatively more efficient in terms of asymptotic variance than the trimmed mean based on regression quantiles. From the parametric point of view, the computationally easy weighted trimmed mean is shown to be an efficient alternative to maximum likelihood estimation which is usually computationally difficult for most underlying distributions except the ideal case of normal ones. From the nonparametric point of view, this weighted trimmed mean is shown to be an efficient alternative robust estimator. A methodology for confidence ellipsoids and hypothesis testing based on the weighted trimmed mean is also introduced.
URI: http://hdl.handle.net/11536/478
ISSN: 1017-0405
期刊: STATISTICA SINICA
Volume: 7
Issue: 3
起始頁: 669
結束頁: 686
顯示於類別:期刊論文