Deriving robust bayesian premiums under bands of prior distributions with applications

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<dc:creator>Sánchez-Sánchez, M.</dc:creator>
<dc:date>2019-01-01</dc:date>
<dc:description xml:lang="es">Sumario: 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.</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/168476.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Matemática del seguro</dc:subject>
<dc:subject xml:lang="es">Teorema de Bayes</dc:subject>
<dc:subject xml:lang="es">Modelo estocástico</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Deriving robust bayesian premiums under bands of prior distributions with applications</dc:title>
<dc:format xml:lang="es">22 p. </dc:format>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 01/01/2019 Volumen 49 Número 1 - enero 2019 , p. 147-168</dc:relation>
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