Jiayang Sun (Chair)

Abstract

In this talk we present a general framework for estimation of regression models with circular covariates, where the conditional distribution of the response given the covariate is specified through a parametric model. The estimation of the conditional mean, or a transformation of it, is carried out nonparametrically, by maximizing the circular local likelihood. The methodology can be extended to joint estimation of conditional mean and dispersion. Important issues are to study bias and variance and to discuss selection of the concentration parameter(s). The generality of the approach is illustrated with several real-data examples, and simulations. (Joint work with Maria Alonso Pena and Rosa Maria Crujeiras Casais, University of Santiago de Compostella, Spain.)