Valid Model-Free Prediction of Future Insurance Claims

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<title>Valid Model-Free Prediction of Future Insurance Claims</title>
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<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20150005984">
<namePart>Hong, Liang</namePart>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20170014706">
<namePart>Martín, Ryan</namePart>
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<dateIssued encoding="marc">2021</dateIssued>
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<abstract displayLabel="Summary">Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction is one such method that can meet this need, and here we tailor that approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.</abstract>
<note type="statement of responsibility">Liang Hong, Ryan Martin</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20120011137">
<topic>Predicciones estadísticas</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080591953">
<topic>Métodos actuariales</topic>
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<title>North American actuarial journal</title>
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<originInfo>
<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
<identifier type="local">MAP20077000239</identifier>
<part>
<text>06/12/2021 Tomo 25 Número 4 - 2021 , p.  473-483</text>
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