Open Access
April, 1995 Estimating the Real Parameter in a Two-Sample Proportional Odds Model
Colin O. Wu
Ann. Statist. 23(2): 376-395 (April, 1995). DOI: 10.1214/aos/1176324526


This paper considers efficient estimation of the Euclidean parameter $\theta$ in the proportional odds model $G(1 - G)^{-1} = \theta F(1 - F)^{-1}$ when two independent i.i.d. samples with distributions $F$ and $G$, respectively, are observed. The Fisher information $I(\theta)$ is calculated based on the solution of a pair of integral equations which are derived from a class of more general semiparametric models. A one-step estimate is constructed using an initial $\sqrt N$-consistent estimate and shown to be asymptotically efficient in the sense that its asymptotic risk achieves the corresponding minimax lower bound.


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Colin O. Wu. "Estimating the Real Parameter in a Two-Sample Proportional Odds Model." Ann. Statist. 23 (2) 376 - 395, April, 1995.


Published: April, 1995
First available in Project Euclid: 11 April 2007

zbMATH: 0829.62031
MathSciNet: MR1332572
Digital Object Identifier: 10.1214/aos/1176324526

Primary: 62F10
Secondary: 62F12 , 62G05 , 62G20

Keywords: Asymptotic efficiency , generalized proportional odds-rate model , one-step estimate , proportional odds model , semiparametric models

Rights: Copyright © 1995 Institute of Mathematical Statistics

Vol.23 • No. 2 • April, 1995
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