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VOL. 57 | 2009 Semiparametric Models and Likelihood - The Power of Ranks


We consider classes of models related to those introduced by Lehmann [Ann. Math. Statist. 24 (1953) 23–43] and Sklar [L’Institut de Statistique de L’Universite de Paris 8 (1959) 229–231]. Recently developed algorithms for finding profile NP likelihood procedures are discussed, extended and implemented for such models by combining them with the MM algorithm. In particular we consider statistical procedures for a regression model with proportional expected hazard rates, and for transformation models including the normal copula. A variety of likelihoods introduced to deal with semiparametric models are considered. They all generate rank results, not only tests, but also estimates, confidence regions, and optimality theory, thereby, to paraphrase Lehmann [Ann. Math. Statist. 24 (1953) 23–43], demonstrating “the power of ranks”.


Published: 1 January 2009
First available in Project Euclid: 3 August 2009

zbMATH: 1271.62066
MathSciNet: MR2681659

Digital Object Identifier: 10.1214/09-LNMS5707

Primary: 62G05, 62G20
Secondary: 62N02

Rights: Copyright © 2009, Institute of Mathematical Statistics


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