Inference for the Top-k Rank List Problem

Peter Hall () and Michael G. Schimek ()
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Peter Hall: The University of Melbourne, Department of Mathematics and Statistics
Michael G. Schimek: Medical University of Graz, Institute for Medical Informatics, Statistics and Documentation

A chapter in COMPSTAT 2008, 2008, pp 433-444 from Springer

Abstract: Abstract Consider a problem where N items (objects or individuals) are judged by assessors using their perceptions of a set of performance criteria, or alternatively by technical devices. In particular, two assessors might rank the items between 1 and N on the basis of relative performance, independently of each other. We aggregate the rank lists in that we assign one if the two assessors agree, and zero otherwise. How far can we continue into this sequence of 0’s and 1’s before randomness takes over? In this paper we suggest methods and algorithms for addressing this problem.

Keywords: ordered list; moderate deviation bound; nonparametric inference; rank aggregation; random degeneration; top-k rank list (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2084-3_36

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DOI: 10.1007/978-3-7908-2084-3_36

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