Factor copula approaches for assessing spatially dependent high-dimensional risks

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      <subfield code="a">Factor copula approaches for assessing spatially dependent high-dimensional risks</subfield>
      <subfield code="c">Leí Hua, Míchelle Xia, and Sanjíb Basu</subfield>
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      <subfield code="a">In this article, we propose an innovative approach for modeling spatial dependence among losses fromvarious geographical locations.
The proposed model converts the challenging task of modeling complex spatial dependence structures into a relatively easier task of
estimating a continuous function, of which the arguments can be the coordinates of the locations. The approach is based on factor copula
models, which can capture various linear and nonlinear dependence.We use radial basis functions as the kernel smoother for estimating
the key function that models all the spatial dependence structures. A case study on a thunderstorm wind loss dataset demonstrates the
analysis and the usefulness of the proposed approach. Extensions to spatiotemporal models and to models for discrete data are briefly
introduced, with an example given for modeling loss frequency with excess zeros.</subfield>
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      <subfield code="a">Modelización mediante cópulas</subfield>
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      <subfield code="t">North American actuarial journal</subfield>
      <subfield code="d">Schaumburg : Society of Actuaries, 1997-</subfield>
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      <subfield code="g">01/03/2017 Tomo 21 Número 1 - 2017 , p. 147-160</subfield>
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