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NON-GAUSSIAN GEOSTATISTICAL MODELING USING (SKEW) T PROCESSES

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dc.creatorCHRISTIAN ELOY CAAMAÑO CARRILLO
dc.date2021
dc.date.accessioned2025-01-10T15:21:38Z
dc.date.available2025-01-10T15:21:38Z
dc.date.issued2021
dc.description.abstractWE PROPOSE A NEW MODEL FOR REGRESSION AND DEPENDENCE ANALYSIS WHEN ADDRESSING SPATIAL DATA WITH POSSIBLY HEAVY TAILS AND AN ASYMMETRIC MARGINAL DISTRIBUTION. WE FIRST PROPOSE A STATIONARY PROCESS WITH T MARGINALS OBTAINED THROUGH SCALE MIXING OF A GAUSSIAN PROCESS WITH AN INVERSE SQUARE ROOT PROCESS WITH GAMMA MARGINALS. WE THEN GENERALIZE THIS CONSTRUCTION BY CONSIDERING A SKEW-GAUSSIAN PROCESS, THUS OBTAINING A PROCESS WITH SKEW-T MARGINAL DISTRIBUTIONS. FOR THE PROPOSED (SKEW) T PROCESS, WE STUDY THE SECOND-ORDER AND GEOMETRICAL PROPERTIES AND IN THE T CASE, WE PROVIDE ANALYTIC EXPRESSIONS FOR THE BIVARIATE DISTRIBUTION. IN AN EXTENSIVE SIMULATION STUDY, WE INVESTIGATE THE USE OF THE WEIGHTED PAIRWISE LIKELIHOOD AS A METHOD OF ESTIMATION FOR THE T PROCESS. MOREOVER WE COMPARE THE PERFORMANCE OF THE OPTIMAL LINEAR PREDICTOR OF THE T PROCESS VERSUS THE OPTIMAL GAUSSIAN PREDICTOR. FINALLY, THE EFFECTIVENESS OF OUR METHODOLOGY IS ILLUSTRATED BY ANALYZING A GEOREFERENCED DATASET ON MAXIMUM TEMPERATURES IN AUSTRALIA.
dc.formatapplication/pdf
dc.identifier.doi10.1111/sjos.12447
dc.identifier.issn1467-9469
dc.identifier.issn0303-6898
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/11645
dc.languagespa
dc.publisherSCANDINAVIAN JOURNAL OF STATISTICS
dc.relation.uri10.1111/sjos.12447
dc.rightsPUBLICADA
dc.titleNON-GAUSSIAN GEOSTATISTICAL MODELING USING (SKEW) T PROCESSES
dc.typeARTÍCULO
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE ESTADISTICA
ubb.SedeCONCEPCIÓN
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