Neural Comput - An estimation of generalized bradley-terry models based on the em algorithm.

Tópicos

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{ method(2212) result(1239) propos(1039) }
{ method(1219) similar(1157) match(930) }
{ framework(1458) process(801) describ(734) }
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{ take(945) account(800) differ(722) }
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Resumo

The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate.

Resumo Limpo

bradleyterri model statist represent one prefer rank data use pairwis comparison result item estim model sever method base sum weight kullbackleibl diverg propos various context purpos letter interpret estim mechan bradleyterri model viewpoint flat fundament notion use inform geometri base point view new estim method propos framework em algorithm propos method differ object function convent method especi treat unobserv comparison consist interpret probabl simplex estim method weight adapt also propos viewpoint sensit experiment result show propos method work appropri weight adapt improv accuraci estim

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