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Recognizing and visualizing copulas : an approach using local Gaussian approximation

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<title>Recognizing and visualizing copulas</title>
<subTitle>: an approach using local Gaussian approximation</subTitle>
<genre authority="marcgt">periodical</genre>
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<dateIssued encoding="marc">2014</dateIssued>
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<abstract displayLabel="Summary">In this paper we examine the relationship between a newly developed local dependence measure, the local Gaussian correlation, and standard copula theory. We are able to describe characteristics of the dependence structure in different copula models in terms of the local Gaussian correlation. Further, we construct a goodness-of-fit test for bivariate copula models. An essential ingredient of this test is the use of a canonical local Gaussian correlation and Gaussian pseudo-observations which make the test independent of the margins, so that it is a genuine test of the copula structure. A Monte Carlo study reveals that the test performs very well compared to a commonly used alternative test. We also propose two types of diagnostic plots which can be used to investigate the cause of a rejected null. Finally, our methods are applied to a classical insurance data set.</abstract>
<note type="statement of responsibility">Geir Drage Berentsen...[]</note>
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<title>Insurance : mathematics and economics</title>
<publisher>Oxford : Elsevier, 1990-</publisher>
<identifier type="issn">0167-6687</identifier>
<identifier type="local">MAP20077100574</identifier>
<text>07/07/2014 Volumen 57 Número 1 - julio 2014 </text>
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<recordCreationDate encoding="marc">140814</recordCreationDate>
<recordChangeDate encoding="iso8601">20140826121631.0</recordChangeDate>
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