Date: 2014-11-07

Time: 15:30-16:30

Location: BURN 1205

Abstract:

We approach the problem of shape constrained regression from a Bayesian perspective. A B-spline basis is used to model the regression function. The smoothness of the regression function is controlled by the order of the B-splines and the shape is controlled by the shape of an associated control polygon. Controlling the shape of the control polygon reduces to some inequality constraints on the spline coefficients. Our approach enables us to take into account combinations of shape constraints and to localize each shape constraint on a given interval. The performances of our method is investigated through a simulation study. Applications to real data sets from the food industry and Global Warming are provided.

Speaker

Khader Khadraoui is an Assistant Professor in the Department of Mathematics and Statistics at Université Laval. He obtained his PhD at the Université de Montpellier 2 in 2011.