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UID:20260602T151928EDT-9852hHDzdI@132.216.98.100
DTSTAMP:20260602T191928Z
DESCRIPTION:Title: Accommodating outlying observations in environmental spa
 tio-temporal processes.\n\nAbstract: In the analysis of most spatiotempora
 l 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 trans
 formation will have an impact on the description of the uncertainty at fut
 ure instants of time or unobserved locations of interest.\n	In this talk I 
 will discuss some of the projects I have been involved with in the last fi
 ve years that relax the assumption of normality of spatiotemporal processe
 s after some suitable transformation of the data. In particular\, I will f
 ocus on a recent proposal that models the variance law of multivariate dyn
 amic linear models. The proposed approach adds flexibility to the usual Mu
 ltivariate Dynamic Gaussian model by defining the process as a scale mixtu
 re between a Gaussian and log-Gaussian processes. The scale is represented
  by a process varying smoothly over space and time which is allowed to dep
 end on covariates. Analysis of artificial datasets show that the parameter
 s are identifiable and simpler models are well recovered by the general pr
 oposed model. The analyses of two important environmental processes\, maxi
 mum temperature and maximum ozone\, illustrate the effectiveness of our pr
 oposal in improving the uncertainty quantification in the prediction of sp
 atio-temporal processes.\n
DTSTART:20230201T203000Z
DTEND:20230201T213000Z
LOCATION:Room 1140\, 91ºÚÁÏÍø College 2001\, CA\, QC\, Montreal\, H3A 1G1\, 2
 001\, avenue 91ºÚÁÏÍø College
SUMMARY:Alexandra Schmidt\, 91ºÚÁÏÍø
URL:/mathstat/channels/event/alexandra-schmidt-mcgill-
 university-345738
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