標題: The symmetric type two-stage trimmed least squares estimator for the simultaneous equations model
作者: Chen, LA
Thompson, P
Hung, HN
統計學研究所
Institute of Statistics
關鍵字: regression quantile;simultaneous equations model;trimmed least squares estimator
公開日期: 1-Oct-2000
摘要: A two-stage symmetric regression quantile is considered as an alternative for estimating the population quantile for the simultaneous equations model. We introduce a two-stage symmetric trimmed least squares estimator (LSE) based on this quantile. It is shown that, under mixed multivariate normal errors, this trimmed LSE has asymptotic variance much closer to the Cramer-Rao lower bound than some usual robust estimators. It can even achieve the Cramer-Rao lower bound when the contaminated variance goes to infinity. This suggests that the symmetric-type quantile function is as efficient in other statistical applications, such as outlier detection. A Monte Carlo study under asymmetric error distribution and a real data analysis are also presented.
URI: http://hdl.handle.net/11536/30247
ISSN: 1017-0405
期刊: STATISTICA SINICA
Volume: 10
Issue: 4
起始頁: 1243
結束頁: 1255
Appears in Collections:Articles