Gaussian proces models for mortality rates and improvement factors
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<title>Gaussian proces models for mortality rates and improvement factors</title>
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<namePart>Risk, Jimmy</namePart>
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<namePart>Zail, Howard</namePart>
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<abstract displayLabel="Summary">We develop a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts.</abstract>
<note type="statement of responsibility">Mike Ludkovski, Jimmy Risk, Howard Zail</note>
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<topic>Cálculo actuarial</topic>
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<topic>Modelos actuariales</topic>
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<topic>Modelos predictivos</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080555306">
<topic>Mortalidad</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20220007825">
<topic>Data driven</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
<part>
<text>03/09/2018 Volumen 48 Número 3 - septiembre 2018 , p. 1307-1347</text>
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