An estimation of generalized Bradley-Terry models based on the em algorithm

Yu Fujimoto, Hideitsu Hino, Noboru Murata

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    2 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)1623-1659
    Number of pages37
    JournalNeural Computation
    Volume23
    Issue number6
    DOIs
    Publication statusPublished - 2011 Jun

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    ASJC Scopus subject areas

    • Cognitive Neuroscience

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