A Flexible bayesian nonparametric model for predicting future insurance claims

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<nonSort xml:space="preserve">A  </nonSort>
<title>Flexible bayesian nonparametric model for predicting future insurance claims</title>
</titleInfo>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20170014706">
<namePart>Martín, Ryan</namePart>
<nameIdentifier>MAPA20170014706</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2017</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">Accurate prediction of future claims is a fundamentally important problem in insurance. The Bayesian approach is natural in this context, as it provides a complete predictive distribution for future claims. The classical credibility theory provides a simple approximation to the mean of that predictive distribution as a point predictor, but this approach ignores other features of the predictive distribution, such as spread, that would be useful for decision making. In this article, we propose a Dirichlet process mixture of log-normals model and discuss the theoretical properties and computation of the corresponding predictive distribution. Numerical examples demonstrate the benefit of our model compared to some existing insurance loss models, and an R code implementation of the proposed method is also provided.</abstract>
<note type="statement of responsibility">Liang Hong, Ryan Martín</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080602437">
<topic>Matemática del seguro</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080579258">
<topic>Cálculo actuarial</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20100065242">
<topic>Teorema de Bayes</topic>
</subject>
<classification authority="">6</classification>
<relatedItem type="host">
<titleInfo>
<title>North American actuarial journal</title>
</titleInfo>
<originInfo>
<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
</originInfo>
<identifier type="issn">1092-0277</identifier>
<identifier type="local">MAP20077000239</identifier>
<part>
<text>05/06/2017 Tomo 21 Número 2 - 2017 , p. 228-241</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">171017</recordCreationDate>
<recordChangeDate encoding="iso8601">20171122122213.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20170033226</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>