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June 1999 Asymptotics when the number of parameters tends to infinity in the Bradley-Terry model for paired comparisons
Gordon Simons, Yi-Ching Yao
Ann. Statist. 27(3): 1041-1060 (June 1999). DOI: 10.1214/aos/1018031267

Abstract

We are concerned here with establishing the consistency and asymptotic normality for the maximum likelihood estimator of a “merit vector” $(u_0,\dots,u_t)$, representing the merits of $t +1$ teams (players, treatments, objects), under the Bradley–Terry model, as $t \to \infty$. This situation contrasts with the well-known Neyman–Scott problem under which the number of parameters grows with $t$ (the amount of sampling), and for which the maximum likelihood estimator fails even to attain consistency. A key feature of our proof is the use of an effective approximation to the inverse of the Fisher information matrix. Specifically, under the Bradley–Terry model, when teams $i$ and $j$ with respective merits $u_i$ and $u_j$ play each other, the probability that team $i$ prevails is assumed to be $u_i/(u_i + u_j)$. Suppose each pair of teams play each other exactly $n$ times for some fixed $n$. The objective is to estimate the merits, $u_i$’s, based on the outcomes of the $nt(t +1)/2$ games. Clearly, the model depends on the $u_i$’s only through their ratios. Under some condition on the growth rate of the largest ratio $u_i/u_j (0 \leq i, j \leq t)$ as $t \to \infty$, the maximum likelihood estimator of $(u_1/u_0,\dots,u_t/u_0)$ is shown to be consistent and asymptotically normal. Some simulation results are provided.

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Gordon Simons. Yi-Ching Yao. "Asymptotics when the number of parameters tends to infinity in the Bradley-Terry model for paired comparisons." Ann. Statist. 27 (3) 1041 - 1060, June 1999. https://doi.org/10.1214/aos/1018031267

Information

Published: June 1999
First available in Project Euclid: 5 April 2002

zbMATH: 0951.62061
MathSciNet: MR1724040
Digital Object Identifier: 10.1214/aos/1018031267

Subjects:
Primary: 60F05
Secondary: 62E20 , 62F12 , 62J15

Keywords: Bradley-Terry model , central limit theorem , consistency , Fisher information matrix , maximum likelihood estimator

Rights: Copyright © 1999 Institute of Mathematical Statistics

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Vol.27 • No. 3 • June 1999
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