Deriving robust bayesian premiums under bands of prior distributions with applications
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<title>Deriving robust bayesian premiums under bands of prior distributions with applications</title>
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<namePart>Sánchez-Sánchez, M.</namePart>
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<abstract displayLabel="Summary">We study the propagation of uncertainty from a class of priors introduced by Arias-Nicolás et al. [(2016) Bayesian Analysis, 11(4), 11071136] to the premiums (both the collective and the Bayesian), for a wide family of premium principles (specifcally, those that preserve the likelihood ratio order). The class under study reflects the prior uncertainty using distortion functions and fulfills some desirable requirements: elicitation is easy, the prior uncertainty can be measured by different metrics, and the range of quantities of interest is easily obtained from the extremal members of the class. We illustrate the methodology with several examples based on different claim counts models.</abstract>
<note type="statement of responsibility">M. Sánchez-Sánchez... [et al.]</note>
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<topic>Matemática del seguro</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20100065242">
<topic>Teorema de Bayes</topic>
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<topic>Modelo estocástico</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>
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<text>01/01/2019 Volumen 49 Número 1 - enero 2019 , p. 147-168</text>
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