<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-05-31T18:46:40Z</responseDate><request verb="GetRecord" identifier="oai:riuma.uma.es:10630/30282" metadataPrefix="mods">https://riuma.uma.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:riuma.uma.es:10630/30282</identifier><datestamp>2026-02-03T10:56:31Z</datestamp><setSpec>com_10630_2254</setSpec><setSpec>col_10630_37953</setSpec></header><metadata><mods:mods xmlns:doc="http://www.lyncode.com/xoai" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
   <mods:name>
      <mods:namePart>Frías, María P.</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Torres-Signes, Antoni</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Ruiz-Medina, María D.</mods:namePart>
   </mods:name>
   <mods:name>
      <mods:namePart>Mateu, Jorge</mods:namePart>
   </mods:name>
   <mods:extension>
      <mods:dateAvailable encoding="iso8601">2024-02-09T11:28:51Z</mods:dateAvailable>
   </mods:extension>
   <mods:extension>
      <mods:dateAccessioned encoding="iso8601">2024-02-09T11:28:51Z</mods:dateAccessioned>
   </mods:extension>
   <mods:originInfo>
      <mods:dateIssued encoding="iso8601">2022</mods:dateIssued>
   </mods:originInfo>
   <mods:identifier type="citation">Frías, M.P., Torres-Signes, A., Ruiz-Medina, M.D. et al. Spatial Cox processes in an infinite-dimensional framework. TEST 31, 175–203 (2022). https://doi.org/10.1007/s11749-021-00773-z</mods:identifier>
   <mods:identifier type="uri">https://hdl.handle.net/10630/30282</mods:identifier>
   <mods:identifier type="doi">10.1007/s11749-021-00773-z</mods:identifier>
   <mods:abstract>We introduce a new class of spatial Cox processes driven by a Hilbert-valued random log-intensity. We adopt a parametric framework in the spectral domain, to estimate its spatial functional correlation structure. Specifically, we consider a spectral functional, approach based on the periodogram operator, inspired on Whittle estimation methodology. Strong consistency of the parametric estimator is proved in the linear case. We illustrate this property in a simulation study under a Gaussian first-order Spatial Autoregressive Hilbertian scenario for the log-intensity model. Our method is applied to the spatial functional prediction of respiratory disease mortality in the Spanish Iberian Peninsula, in the period 1980–2015.</mods:abstract>
   <mods:language>
      <mods:languageTerm>eng</mods:languageTerm>
   </mods:language>
   <mods:accessCondition type="useAndReproduction">open access</mods:accessCondition>
   <mods:subject>
      <mods:topic>Medicina - Modelos matemáticos</mods:topic>
   </mods:subject>
   <mods:titleInfo>
      <mods:title>Spatial Cox processes in an infinite-dimensional framework</mods:title>
   </mods:titleInfo>
   <mods:genre>journal article</mods:genre>
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