Comparison of Normal Variance Estimators under Multiple Criteria and Towards a Compromise Estimator

doi:10.1080/00949650410001729490

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/98648

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題名:	Comparison of Normal Variance Estimators under Multiple Criteria and Towards a Compromise Estimator
作者:	Lin, Jyh-Jiuan;Pal, Nabendu
貢獻者:	淡江大學統計學系
關鍵詞:	Affine equivariance;Risk function;chi-square distribution
日期:	2005-08
上傳時間:	2014-09-03 19:42:07 (UTC+8)
出版者:	Abingdon: Taylor & Francis
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.
關聯:	Journal of Statistical Computation and Simulation 75(8), pp.645-665
DOI:	10.1080/00949650410001729490
顯示於類別:	[統計學系暨研究所] 期刊論文

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