Title: Some recent progress on nonlinear spatial modelling: A review
Authors: Zudi Lu - University of Southampton (United Kingdom) [presenting]
Abstract: Larger amounts of spatial or spatio-temporal data with more complex structures collected at irregularly spaced sampling locations are prevalent in a wide range of disciplines. With few exceptions, however, practical statistical methods for nonlinear modeling and analysis of such data remain elusive. A review is provided on some developments and progress of our research. In particular, we will look at some nonparametric methods for probability, including joint, density estimation, and semiparametric models for a class of spatio-temporal autoregressive partially nonlinear regression models permitting possibly nonlinear relationships between responses and covariates. In the setting of semiparametric spatio-temporal modelling, we will also show a computationally feasible data-driven method for spatial weight matrix estimation. For illustration, our methodology is applied to investigate some land and housing prices data sets.