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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/19752


    Title: Generalized subset selection procedures under heteroscedasticity
    Authors: Chang, Yi-ping;黃文濤;Huang, Wen-tao
    Contributors: 淡江大學經營決策學系
    Keywords: Ranking and selection;Generalized subset selection procedure;Generalized probability of correct selection;Quantile;Signal-to-noise ratio 1. Introduction
    Date: 2001-10-01
    Issue Date: 2009-11-30 12:20:46 (UTC+8)
    Publisher: Elsevier
    Abstract: In this paper, we propose and study a generalized subset selection procedure for selecting the best population. Based on the concept of generalized subset selection procedure, some selection procedures for normal populations are proposed and studied. They are used, respectively, to select the best population (populations) with respect to the largest mean, the largest pth quantile and the largest signal-to-noise ratio. For the case of common unknown variance, the proposed generalized subset selection procedure for selecting the largest mean becomes exactly the same as that has been given in Hsu (in: T.J. Santner, A.C. Tamhane (Eds.), Design of Experiments: Ranking and Selection, Marcel Dekker, New York, 1984, pp. 179–198). A Monte Carlo study shows that the proposed generalized subset selection procedures behave satisfactorily. An illustration of a set of real data is also given.
    Relation: Journal of Statistical Planning and Inference 98(1-2), pp.239-258
    DOI: 10.1016/S0378-3758(00)00305-0
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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