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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98648


    Title: Comparison of Normal Variance Estimators under Multiple Criteria and Towards a Compromise Estimator
    Authors: Lin, Jyh-Jiuan;Pal, Nabendu
    Contributors: 淡江大學統計學系
    Keywords: Affine equivariance;Risk function;chi-square distribution
    Date: 2005-08
    Issue Date: 2014-09-03 19:42:07 (UTC+8)
    Publisher: Abingdon: Taylor & Francis
    Abstract: 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.
    Relation: Journal of Statistical Computation and Simulation 75(8), pp.645-665
    DOI: 10.1080/00949650410001729490
    Appears in Collections:[統計學系暨研究所] 期刊論文

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