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Activity Number: 181
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #316601
Title: Distance-Based Models for Big Ranking Data
Author(s): Philip L.H. Yu* and Hang Xu
Companies: The University of Hong Kong and The University of Hong Kong
Keywords: Ranking data ; Kendall distance ; Spearman distance
Abstract:

Distance-based models for ranking data postulates that the probability of observing a ranking of a set of objects depends on how close the ranking is from an unknown modal ranking according to a distance measure. In other words, the probability of the ranking is the greatest at the modal ranking and it will decay the further it is away from the modal ranking. In the model, the rate of decay is governed by a parameter. However, when the number of objects becomes large, the determination of the modal ranking may not be accurate and the computation of normalizing constant may become infeasible for some distance measures. In this talk, we introduce a new estimation procedure for the distance-based models based on various distance measures including Kendall, Spearman and Hamming. New method is proposed to approximate the normalizing constant.


Authors who are presenting talks have a * after their name.

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