題目：Gaussian process methods for nonparametric functional regression with mixed
In this talk Gaussian process methods are introduced for nonparametric functional regression for both scalar and functional responses with mixed multidimensional functional and scalar predictors.
The proposed models allow the response variables to depend on the entire trajectories of the functional predictors. They inherit the desirable properties of Gaussian process regression, and can naturally accommodate both scalar and functional variables as the predictors, as well as easy to obtain and express uncertainty in predictions.
The numerical experiments show that the proposed methods significantly outperform the competing models, and their usefulness is also demonstrated by the application.
Bo Wang is an Associate Professor in Statistics in the School of Computing and Mathematical Sciences, University of Leicester, UK. He received his BSc in Mathematics in 1994, the MSc in Operational Research and Control Theory in 1997 and the PhD in Applied Mathematics in 2000, all from Shandong University of China. His research interests include statistical modelling, computational statistics, machine learning, and mortality modelling and forecasting. He has authored more than 40 papers, many of which were published in prestigious statistical or machine learning journals. His paper published in the Journal of Nonparametric Statistics received JNPS 2011 Best Paper Award. He also received University of Leicester Citizen Recognition Scheme Award in 2023. Dr Wang is an investigator of several projects funded by Institute and Faculty of Actuaries, European Union and the University of Leicester. He is a reviewer for more than 80 journals/funding bodies, and has been a keynote speaker or invited speaker in a number of high profile international conferences. He is the director of Mathematics of Dalian Leicester Institute, an associate editor and guest editor of Frontiers in Applied Mathematics and Statistics, an editorial board member and guest editor of Mathematics, Fellow of Royal Statistical Society, Fellow of Higher Education Academy, and member of Asia-Pacific Artificial Intelligence Association (AAIA).