香蕉视频

Title:聽Accommodating outlying observations in environmental spatio-temporal processes.

础产蝉迟谤补肠迟:听In the analysis of most spatiotemporal processes in environmental studies, observations present distributions that are not normal. Commonly, some transformation is applied to the data and inference is performed at the transformed scale. Commonly, the transformation will have an impact on the description of the uncertainty at future instants of time or unobserved locations of interest.
In this talk I will discuss some of the projects I have been involved with in the last five years that relax the assumption of normality of spatiotemporal processes after some suitable transformation of the data. In particular, I will focus on a recent proposal that models the variance law of multivariate dynamic linear models. The proposed approach adds flexibility to the usual Multivariate Dynamic Gaussian model by defining the process as a scale mixture between a Gaussian and log-Gaussian processes. The scale is represented by a process varying smoothly over space and time which is allowed to depend on covariates. Analysis of artificial datasets show that the parameters are identifiable and simpler models are well recovered by the general proposed model. The analyses of two important environmental processes, maximum temperature and maximum ozone, illustrate the effectiveness of our proposal in improving the uncertainty quantification in the prediction of spatio-temporal processes.