In Bayesian spatially varying parameter models the covariates' coefficients in a regression model are allowed to change smoothly in space. A Markov random field is adopted as an improper prior distribution for the area-specific spatial effects. We demonstrate that the posterior distribution is a...

In this article, we propose a new parametric family of models for real-valued spatio-temporal stochastic processes <b>"S"</b>(<b>"x"</b>, <b>"t"</b>) and show how low-rank approximations can be used to overcome the computational problems that arise in fitting the proposed class of models to large datasets. Separable...

By treating the conditional approach that was suggested by Diggle and Rowlingson as a generalized additive model, we provide a semiparametric method for point process modelling with point source interventions. We illustrate the flexibility of this approach with two applications. The first is a...

In this article, we propose a method for conducting likelihood-based inference for a class of nonstationary spatiotemporal log-Gaussian Cox processes. The method uses convolution-based models to capture spatiotemporal correlation structure, is computationally feasible even for large datasets,...