A Credibility approach for combining likelihoods of generalized linear models

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<title>Credibility approach for combining likelihoods of generalized linear models</title>
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<namePart>Schinzinger, Edo</namePart>
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<abstract displayLabel="Summary">Generalized linear models are a popular tool for the modelling of insurance claims data. Problems arise with the model fitting if little statistical information is available. In case that related statistics are available, statistical inference can be improved with the help of the borrowing-strength principle. We present a credibility approach that combines the maximum likelihood estimators of individual canonical generalized linear models in a meta-analytic way to an improved credibility estimator. We follow the concept of linear empirical Bayes estimation, which reduces the necessary parametric assumptions to a minimum. The concept is illustrated by a simulation study and an application example from mortality modelling.</abstract>
<note type="statement of responsibility">Marcus C. Christiansen and Edo Schinzinger</note>
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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>
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<text>01/09/2016 Volumen 46 Número 3 - septiembre 2016 , p. 531-569</text>
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