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A nonparametric procedure for testing partially ranked data

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  • Jyh-Shyang Wu
  • Wen-Shuenn Deng

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.

Suggested Citation

  • Jyh-Shyang Wu & Wen-Shuenn Deng, 2017. "A nonparametric procedure for testing partially ranked data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 29(2), pages 213-230, April.
    • Handle: RePEc:taf:gnstxx:v:29:y:2017:i:2:p:213-230
    DOI: 10.1080/10485252.2017.1303055
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    References listed on IDEAS

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    1. P.B. Brockhoff & D.J. Best & J.C.W. Rayner, 2004. "An Application of Extended Analysis for Ranked Data with Ties," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 46(2), pages 197-204, June.
    2. D'Elia, Angela & Piccolo, Domenico, 2005. "A mixture model for preferences data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 917-934, June.
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