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