BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Boris Beranger (School of Mathematics and Statistics\, University New South Wales\, Sydney) DTSTART:20201015T100000Z DTEND:20201015T110000Z DTSTAMP:20260423T003234Z UID:ProbStat/10 DESCRIPTION:Title: High-dimensional inference for max-stable processes\nby Boris Berang er (School of Mathematics and Statistics\, University New South Wales\, Sy dney) as part of Probability & Statistics (IST-CEMAT\, FC-CEAUL\, ULisbon )\n\n\nAbstract\nDroughts\, high temperatures and strong winds are key cau ses of the recent bushfires that have touched a major part of the Australi an territory. Such extreme events seem to appear with increasing frequency \, creating an urgent need to better understand the behaviour of extreme e nvironmental phenomena. Max-stable processes are a widely popular tool to model spatial extreme events with several flexible models available in the literature. For inference on max-stable models\, exact likelihood estimat ion becomes quickly computationally intractable as the number of spatial l ocations grows\, limiting their applicability to large study regions or fi ne grids. In this talk\, we introduce two methodologies based on composite likelihoods\, to circumvent this issue. First\, we assume the occurrence times of maxima available in order to incorporate the Stephenson-Tawn conc ept into the composite likelihood framework. Second\, we propose to aggreg ate the information between locations into histograms and to derive a comp osite likelihood variation for these summaries. The significant improvemen ts in performance of each estimation procedures is established through sim ulation studies and illustrated on two temperature datasets from Australia .\n\nJoint seminar CEMAT and CEAUL\n LOCATION:https://researchseminars.org/talk/ProbStat/10/ END:VEVENT END:VCALENDAR