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Astin Bulletin
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Article . 2018
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A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

A mixture model for payments and payment numbers in claims reserving
Authors: GIGANTE, PATRIZIA; PICECH, LIVIANA; Sigalotti, Luciano;

A MIXTURE MODEL FOR PAYMENTS AND PAYMENT NUMBERS IN CLAIMS RESERVING

Abstract

AbstractWe consider a Tweedie's compound Poisson regression model with fixed and random effects, to describe the payment numbers and the incremental payments, jointly, in claims reserving. The parameter estimates are obtained within the framework of hierarchical generalized linear models, by applying theh-likelihood approach. Regression structures are allowed for the means and also for the dispersions. Predictions and prediction errors of the claims reserves are evaluated. Through the parameters of the distributions of the random effects, some external information (e.g. a development pattern of industry wide-data) can be incorporated into the model. A numerical example shows the impact of external data on the reserve and prediction error evaluations.

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Italy
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Keywords

Applications of statistics to actuarial sciences and financial mathematics, hierarchical generalized linear models, Generalized linear models (logistic models), claims reserving, conditional mean square error of prediction, Claims reserving, Tweedie’s compound Poisson model, h-likelihood, Tweedie's compound Poisson model, hierarchical generalized linear model, Risk theory, insurance, Claims reserving; Tweedie’s compound Poisson model; conditional mean square error of prediction; hierarchical generalized linear models; h-likelihood, \(h\)-likelihood

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green