Linear trimmed means for the linear regression with AR(1) errors model
| dc.citation.epage | 3467 | en_US |
| dc.citation.issue | 11 | en_US |
| dc.citation.spage | 3457 | en_US |
| dc.citation.volume | 140 | en_US |
| dc.citation.woscount | 0 | |
| dc.contributor.author | Lai, Yi-Hsuan | en_US |
| dc.contributor.author | Chen, Lin-An | en_US |
| dc.contributor.author | Tang, Chau-Shyun | en_US |
| dc.contributor.department | 統計學研究所 | zh_TW |
| dc.contributor.department | 管理科學系 | zh_TW |
| dc.contributor.department | Institute of Statistics | en_US |
| dc.contributor.department | Department of Management Science | en_US |
| dc.date.accessioned | 2014-12-08T15:48:03Z | |
| dc.date.available | 2014-12-08T15:48:03Z | |
| dc.date.issued | 2010-11-01 | en_US |
| dc.description.abstract | For the linear regression with AR(1) errors model, the robust generalized and feasible generalized estimators of Lai et al. (2003) of regression parameters are shown to have the desired property of a robust Gauss Markov theorem. This is done by showing that these two estimators are the best among classes of linear trimmed means. Monte Carlo and data analysis for this technique have been performed. (C) 2010 Elsevier B.V. All rights reserved. | en_US |
| dc.identifier.doi | 10.1016/j.jspi.2010.05.015 | en_US |
| dc.identifier.issn | 0378-3758 | en_US |
| dc.identifier.journal | JOURNAL OF STATISTICAL PLANNING AND INFERENCE | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.jspi.2010.05.015 | en_US |
| dc.identifier.uri | https://ir.lib.nycu.edu.tw/handle/11536/32041 | |
| dc.identifier.wosnumber | WOS:000279997000045 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Gauss Markov theorem | en_US |
| dc.subject | Generalized least squares estimator | en_US |
| dc.subject | Linear trimmed mean | en_US |
| dc.subject | Robust estimator | en_US |
| dc.title | Linear trimmed means for the linear regression with AR(1) errors model | en_US |
| dc.type | Article | en_US |