1 Ocak 2010 Dergi makalesi Açık Erişim

Gencaga, Deniz; Kuruoglu, Ercan E.; Ertuzun, Aysin

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/27957</identifier>
  <creators>
    <creator>
      <creatorName>Gencaga, Deniz</creatorName>
      <givenName>Deniz</givenName>
      <familyName>Gencaga</familyName>
    </creator>
    <creator>
      <creatorName>Kuruoglu, Ercan E.</creatorName>
      <givenName>Ercan E.</givenName>
      <familyName>Kuruoglu</familyName>
      <affiliation>CNR, Area Ric, ISTI, I-56124 Pisa, Italy</affiliation>
    </creator>
    <creator>
      <creatorName>Ertuzun, Aysin</creatorName>
      <givenName>Aysin</givenName>
      <familyName>Ertuzun</familyName>
      <affiliation>Bogazici Univ, Elect &amp; Elect Engn Dept, TR-34342 Istanbul, Turkey</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Modeling Non-Gaussian Time-Varying Vector Autoregressive Processes By Particle Filtering</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2010</publicationYear>
  <dates>
    <date dateType="Issued">2010-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/27957</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/s11045-009-0081-8</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">We present a novel and general methodology for modeling time-varying vector autoregressive processes which are widely used in many areas such as modeling of chemical processes, mobile communication channels and biomedical signals. In the literature, most work utilize multivariate Gaussian models for the mentioned applications, mainly due to the lack of efficient analytical tools for modeling with non-Gaussian distributions. In this paper, we propose a particle filtering approach which can model non-Gaussian autoregressive processes having cross-correlations among them. Moreover, time-varying parameters of the process can be modeled as the most general case by using this sequential Bayesian estimation method. Simulation results justify the performance of the proposed technique, which potentially can model also Gaussian processes as a sub-case.</description>
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