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Parameter estimation for generalized thurstone choice models

Vojnovic, Milan and Yun, Seyoung (2016) Parameter estimation for generalized thurstone choice models. Proceedings of Machine Learning Research, 48. pp. 498-506. ISSN 1938-7228

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Abstract

We consider the maximum likelihood parameter estimation problem for a generalized Thurstone choice model, where choices are from comparison sets of two or more items. We provide tight characterizations of the mean square error, as well as necessary and sufficient conditions for correct classification when each item belongs to one of two classes. These results provide insights into how the estimation accuracy depends on the choice of a generalized Thurstone choice model and the structure of comparison sets. We find that for a priori unbiased structures of comparisons, e.g., when comparison sets are drawn independently and uniformly at random, the number of observations needed to achieve a prescribed estimation accuracy depends on the choice of a generalized Thurstone choice model. For a broad set of generalized Thurstone choice models, which includes all popular instances used in practice, the estimation error is shown to be largely insensitive to the cardinality of comparison sets. On the other hand, we found that there exist generalized Thurstone choice models for which the estimation error decreases much faster with the cardinality of comparison sets.

Item Type: Article
Official URL: http://proceedings.mlr.press/v48/
Additional Information: © 2016 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 23 Nov 2017 11:18
Last Modified: 20 May 2019 02:13
URI: http://eprints.lse.ac.uk/id/eprint/85703

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