A Functional Single-Index Model

作者:sds-admin 发布时间:2019-07-15

Speaker: Jiguo Cao, Simon Fraser University, CA
Host: Xiaolei Xun, School of Data Science, Fudan University
Time: 14:00-15:00, July 16, 2019
Location: Zibin N102, Fudan University
Abstract: We propose a semiparametric functional single-index model for studying the relationship between a univariate response and multiple functional covariates. The parametric part of the model integrates a functional linear regression model and a sufficient dimension-reduction structure. The nonparametric part of the model allows the response-index dependence or the link function to be unspecified. The B-spline method is used to approximate the coefficient function, which leads to a dimension-folding-type model. A new kernel regression method is developed to handle the dimension-folding model, allowing us to estimate the index vector and the B-spline coefficients efficiently. We also establish the asymptotic properties and semiparametric optimality for the estimators.
Bio: Dr. Jiguo Cao is the Canada Research Chair in Data Science at the Department of Statistics and Actuarial Science, Director of Pacific Blue Cross Health Informatics Laboratory and the Associate Faculty Member at School of Computing Science, at Simon Fraser University. Dr. Cao’s research interests include functional data analysis and estimating differential equation models.