Hodges-Lehmann Inverse Likelihood Estimators
Kjell Doksum
Department of Statistics, University of Wisconsin, Madison, United States

Hodges and Lehmann proposed using rank test statistics evaluated at inversely transformed data to construct estimating equations. In the context of a model of the form Y=h(e;z,beta), where e is a random error, this approach corresponds to computing the inverse e=g(Y,z;beta) by solving Y=h(e:z,beta) for e and using the distribution of the ranks of independent e's as a likelihood. The properties of the resulting estimators have been developed in many important contexts. This talk will review and extend asymptotic optimality properties of Hodges Lehmann estimators in semi-parametric models.

Keywords: Hodges-Lehmann estimators; Asymptotic efficiency; Ranks; Likelihood

Biography: Kjell Doksum is Emeritus Professor at the University of California, Berkeley, and Research Scholar at the University of Wisconsin, Madison, USA