標題: Symmetric regression quantile and its application to robust estimation for the nonlinear regression model
作者: Chen, LA
Tran, LT
Lin, LC
統計學研究所
Institute of Statistics
關鍵字: nonlinear regression;regression quantile;trimmed mean
公開日期: 1-Dec-2004
摘要: Populational conditional quantiles in terms of percentage alpha are useful as indices for identifying outliers. We propose a class of symmetric quantiles for estimating unknown nonlinear regression conditional quantiles. In large samples, symmetric quantiles are more efficient than regression quantiles considered by Koenker and Bassett (Econometrica 46 (1978) 33) for small or large values of alpha, when the underlying distribution is symmetric, in the sense that they have smaller asymptotic variances. Symmetric quantiles play a useful role in identifying outliers. In estimating nonlinear regression parameters by symmetric trimmed means constructed by symmetric quantiles, we show that their asymptotic variances can be very close to (or can even attain) the Cramer-Rao lower bound under symmetric heavy-tailed error distributions, whereas the usual robust and nonrobust estimators cannot. (C) 2003 Elsevier B.V. All rights reserved.
URI: http://dx.doi.org/10.1016/j.jspi.2003.09.014
http://hdl.handle.net/11536/25622
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2003.09.014
期刊: JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume: 126
Issue: 2
起始頁: 423
結束頁: 440
Appears in Collections:Articles


Files in This Item:

  1. 000224224300003.pdf