Title: Estimation in Semiparametric Models with Unspecified Dependence Structures

Speaker: Xeuming He, University of Illiniois

ABSTRACT: We consider M-estimators for partly linear models with possibly dependent observations such as those from a longitudinal study. We approximate the nonparametric function in the model by a regression spline and show that any M-estimation algorithm for the usual linear models can be used to obtain consistent estimates of the semiparametric model and valid large sample inferences on the linear components without any specification of the error distribution and the covariance structure. Included as special cases are the analysis of the conditional mean and median functions for longitudinal data and for certain spatial data. Advantages of this approach from both the theoretical and practical points of view are discussed in the talk.