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    题名: A nonparametric procedure for testing partially ranked data
    作者: Jyh-Shyang Wu;Wen-Shuenn Deng
    关键词: Anderson's test;partially ranked data;chi-square test;imposed rank;contingency table;U-statistics;root-n local alternatives
    日期: 2017-03-20
    上传时间: 2017-04-19 02:10:12 (UTC+8)
    出版者: Taylor and Francis
    摘要: 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.
    關聯: Journal of Nonparametric Statistics 29(2), p.213-230
    DOI: 10.1080/10485252.2017.1303055
    显示于类别:[統計學系暨研究所] 期刊論文

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