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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/110196

    Title: A nonparametric procedure for testing partially ranked data
    Authors: Jyh-Shyang Wu;Wen-Shuenn Deng
    Keywords: Anderson's test;partially ranked data;chi-square test;imposed rank;contingency table;U-statistics;root-n local alternatives
    Date: 2017-03-20
    Issue Date: 2017-04-19 02:10:12 (UTC+8)
    Publisher: Taylor and Francis
    Abstract: In consumer preference studies, it is common to seek a complete ranking of a variety of, say N, alternatives or treatments. Unfortunately, as N increases, it becomes progressively more confusing and undesirable for respondents to rank all N alternatives simultaneously. Moreover, the investigators may only be interested in consumers’ top few choices. Therefore, it is desirable to accommodate the setting where each survey respondent ranks only her/his most preferred k (k < N) alternatives. In this paper, we propose a simple procedure to test the independence of N alternatives and the top-k ranks, such that the value of k can be predetermined before securing a set of partially ranked data or be at the discretion of the investigator in the presence of complete ranking data. The asymptotic distribution of the proposed test under root-n local alternatives is established. We demonstrate our procedure with two real data sets.
    Relation: Journal of Nonparametric Statistics 29(2), p.213-230
    DOI: 10.1080/10485252.2017.1303055
    Appears in Collections:[統計學系暨研究所] 期刊論文

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