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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/69149


    Title: A Procedure of Selecting All Good Populations Under Heteroscedasticity
    Authors: Wu, Shu-Fei;Yu, Yuh-Ru
    Contributors: 淡江大學統計學系
    Keywords: One-stage;Percentage points;Percentage points;Select all good populations whose mean does not fall too much below the best one;Simultaneous inference;Student t distribution;Two-stage
    Date: 2008-10
    Issue Date: 2013-05-31 11:38:12 (UTC+8)
    Publisher: Philadelphia: Taylor & Francis Inc.
    Abstract: Suppose that k normal populations with means μ1,…, μ k and variances are considered. When variances are unknown and equal, Lam (1986) proposed a procedure to select all good populations, where a population is good if μ i ≥ μ[k] − ϵ and μ[k] is the largest normal mean among k normal means. When variances are unknown and possibly unequal, a two-stage procedure selecting all good populations such that the probability of correct selection being at least a pre-specified value of P* is proposed. Without decreasing the probability of correct selection, the confidence interval of pairwise differences of all means to the largest normal mean is presented with the selection procedure. When the additional samples at the second stage could be costly, a data-analysis one-stage procedure selecting all good population is proposed. At last, one real-life example is given to illustrate all procedures.
    Relation: Communications in Statistics - Simulation and Computation 38(1), pp.46-57
    DOI: 10.1080/03610910802395661
    Appears in Collections:[統計學系暨研究所] 期刊論文

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