A Parameterized approach to modeling and forecasting mortality

<?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">MAP20090091634</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20091022125409.0</controlfield>
    <controlfield tag="008">091019e20090227esp|| p      |0|||b|spa 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">6</subfield>
    </datafield>
    <datafield tag="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20090035966</subfield>
      <subfield code="a">Hatzopoulos, P.</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="2">
      <subfield code="a">A Parameterized approach to modeling and forecasting mortality</subfield>
      <subfield code="c">P. Hatzopoulos, S. Haberman</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">A new method is proposed of constructing mortality forecasts. This parameterized approach utilizes Generalized Linear Models (GLMs), based on heteroscedastic Poisson (non-additive) error structures, and using an orthonormal polynomial design matrix. Principal Component (PC) analysis is then applied to the cross-sectional fitted parameters. The produced model can be viewed either as a one-factor parameterized model where the time series are the fitted parameters, or as a principal component model, namely a log-bilinear hierarchical statistical association model of Goodman [Goodman, L.A., 1991. Measures, models, and graphical displays in the analysis of cross-classified data. J. Amer. Statist. Assoc. 86(416), 10851111] or equivalently as a generalized LeeCarter model with p interaction terms. Mortality forecasts are obtained by applying dynamic linear regression models to the PCs. Two applications are presented: Sweden (17512006) and Greece (19572006).Article O</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="1">
      <subfield code="0">MAPA20080555306</subfield>
      <subfield code="a">Mortalidad</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="1">
      <subfield code="0">MAPA20090033023</subfield>
      <subfield code="a">Estadística matemática</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="1">
      <subfield code="0">MAPA20080549923</subfield>
      <subfield code="a">Bootstrap</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="1">
      <subfield code="0">MAPA20080580377</subfield>
      <subfield code="a">Esperanza de vida</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="1">
      <subfield code="0">MAPA20080579258</subfield>
      <subfield code="a">Cálculo actuarial</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20090035973</subfield>
      <subfield code="a">Haberman, S.</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077100574</subfield>
      <subfield code="t">Insurance : mathematics and economics</subfield>
      <subfield code="d">Oxford : Elsevier, 1990-</subfield>
      <subfield code="x">0167-6687</subfield>
      <subfield code="g">27/02/2009 Tomo 44 Número 1  - 2009, p. 103-123</subfield>
    </datafield>
  </record>
</collection>