期刊論文

標題 Comparison of Normal Variance Estimators under Multiple Criteria and Towards a Compromise Estimator
學年 94
學期 1
出版(發表)日期 2005/08/01
作品名稱 Comparison of Normal Variance Estimators under Multiple Criteria and Towards a Compromise Estimator
作品名稱(其他語言)
著者 Lin, Jyh-Jiuan; Pal, Nabendu
單位 淡江大學統計學系
出版者 Abingdon: Taylor & Francis
著錄名稱、卷期、頁數 Journal of Statistical Computation and Simulation 75(8), pp.645-665
摘要 For estimating a normal variance under the squared error loss function it is well known that the best affine (location and scale) equivariant estimator, which is better than the maximum likelihood estimator as well as the unbiased estimator, is also inadmissible. The improved estimators, e.g., stein type, brown type and Brewster–Zidek type, are all scale equivariant but not location invariant. Lately, a good amount of research has been done to compare the improved estimators in terms of risk, but comparatively less attention had been paid to compare these estimators in terms of the Pitman nearness criterion (PNC) as well as the stochastic domination criterion (SDC). In this paper, we have undertaken a comprehensive study to compare various variance estimators in terms of the PNC and the SDC, which has been long overdue. Finally, using the results for risk, the PNC and the SDC, we propose a compromise estimator (sort of a robust estimator) which appears to work ‘well’ under all the criteria discussed above.
關鍵字 Affine equivariance; Risk function; chi-square distribution
語言 英文
ISSN 0094-9655;1563-5163
期刊性質 國外
收錄於 SCI;SSCI;EI
產學合作
通訊作者 Lin, Jyh-Jiuan
審稿制度
國別 英國
公開徵稿
出版型式 紙本;電子版