
Abstract
The aim of the present paper is to apply a recently developed quantile approach for lattice-valued data to the special case of ranking data. We show how to analyze profiles of total orders by means of lattice-valued quantiles and thereby develop new methods of descriptive data analysis for ranking data beyond known methods like permutation polytopes or multidimensional scaling. We furthermore develop an aggregation rule for social profiles (, called commonality sharing, here,) that selects from a given profile that ordering(s) that is (are) most centered in the profile, where the notion of centrality and outlyingness are based on purely order-theoretic concepts. Finally, we sketch, how one can use the quantile approach to establish tests of model fit for statistical models of ranking data.
Item Type: | Paper |
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Keywords: | complete lattice, quantile, outlyingness, descriptive data analysis, social profile, social choice theory, consensus rule, commonality sharing |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
Subjects: | 500 Science > 500 Science 500 Science > 510 Mathematics |
URN: | urn:nbn:de:bvb:19-epub-40452-9 |
Language: | English |
Item ID: | 40452 |
Date Deposited: | 18. Sep 2017, 15:19 |
Last Modified: | 04. Nov 2020, 13:17 |