- 【Updated on May 12, 2025】 Integration of CiNii Dissertations and CiNii Books into CiNii Research
- Trial version of CiNii Research Knowledge Graph Search feature is available on CiNii Labs
- Suspension and deletion of data provided by Nikkei BP
- Regarding the recording of “Research Data” and “Evidence Data”
An Estimation of Generalized Bradley-Terry Models Based on the <i>em</i> Algorithm
-
- Yu Fujimoto
- Department of Integrated Information Technology, Aoyama Gakuin University, Chuo, Sagamihara, Kanagawa 252-5258, Japan
-
- Hideitsu Hino
- School of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan
-
- Noboru Murata
- School of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan
Search this article
Description
<jats:p> 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. </jats:p>
Journal
-
- Neural Computation
-
Neural Computation 23 (6), 1623-1659, 2011-06
MIT Press - Journals
