Pesquisa de referências

Bivariate phase-type distributions for experience rating in disability insurance

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20260012124</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20260422175110.0</controlfield>
    <controlfield tag="008">260421e20260413che|||p      |0|||b|eng d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">11</subfield>
    </datafield>
    <datafield tag="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20260007175</subfield>
      <subfield code="a">Furrer, Christian</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Bivariate phase-type distributions for experience rating in disability insurance</subfield>
      <subfield code="c">Christian Furrer, Jacob Juhl Sørensen and Jorge Yslas</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">The article develops advanced mixed Poisson regression models applied to experience rating in disability insurance. It introduces hierarchical gamma distributions and multivariate phase-type distributions to model heterogeneity and dependence between disability and recovery rates. Estimation algorithms based on EM and ECM are presented, together with a simulation study comparing the predictive performance of the different approaches. The results show that phase-type models offer greater flexibility and improved predictive performance, especially when complex dependencies exist between latent group effects</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080603793</subfield>
      <subfield code="a">Seguro de incapacidad</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080564322</subfield>
      <subfield code="a">Tarificación</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080592011</subfield>
      <subfield code="a">Modelos actuariales</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080577841</subfield>
      <subfield code="a">Riesgo aleatorio</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080560447</subfield>
      <subfield code="a">Rendimiento</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20260007182</subfield>
      <subfield code="a">Sørensen, Jacob Juhl</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20260007199</subfield>
      <subfield code="a">Yslas, Jorge</subfield>
    </datafield>
    <datafield tag="710" ind1="2" ind2=" ">
      <subfield code="0">MAPA20180008764</subfield>
      <subfield code="a">Springer</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20220007085</subfield>
      <subfield code="g">13/04/2026 Número 16 issue 1 - abril 2026 , 37 p.</subfield>
      <subfield code="t">European Actuarial Journal</subfield>
      <subfield code="d">Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</subfield>
    </datafield>
  </record>
</collection>