Prof. Jianqing Shi
Southern University of Science and Technology, Fellow of The Royal Statistical Society, Turing Fellow of Alan Turing Institute in UK
Title: Wrapped Gaussian Process Functional Regression Model for Batch Data on Riemannian Manifold
Regression is an essential and fundamental methodology in statistical analysis. The majority of the literature focuses on linear and nonlinear regression in the context of the
Euclidean space. However, regression models in non-Euclidean spaces deserve more attention due to collection of increasing volumes of manifold-valued data. In this context,
we proposed a concurrent functional regression model for batch data on Riemannian manifolds by estimating both mean structure and covariance structure simultaneously. The response variable is assumed to follow a wrapped Gaussian process distribution. Nonlinear relationships between manifold-valued response variables and multiple Euclidean covariates can be captured by this model in which the covariates can be functional and/or scalar. The performance of the model has been tested on both simulated data and real data, showing it is an effective and efficient tool in conducting functional data regression on Riemannian manifolds.
Dr. Jian Qing SHI, the Professor of Department of Statistics and Data Science, Southern University of Science and Technology. He used to be Reader in Statistics of Newcastle University and Assistant Director of Cloud Computing for Big Data CDT. He is Turing Fellow of Alan Turing Institute in UK. His research interests include Regression analysis, Canonical correlation analysis, Machine learning. He has published many papers in the top journals in statistics include the Journal of Royal Statistical Society Series B, Journal of American Statistical Association, Biometrika, Biometrics and Biostatistics. In 2012, Dr. SHI received Health Innovation Challenge Fund with PI Prof. J A Eyre (total of 2.1 million pounds, Jan. 2012—Jan.2015). In 2011, he published a book Gaussian Process Regression Analysis for Functional Data with Choi. T. in Chapman & Hall, CRC.