Reference : Smoothed nonparametric maximum likelihood estimation of the risk distribution underlying...

	Document type :	Scientific journals : Article
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Mathematics
	To cite this reference:	http://hdl.handle.net/2268/24517

Title :	Smoothed nonparametric maximum likelihood estimation of the risk distribution underlying bonus-malus systems
Language :	English
Author, co-author :	Denuit, Michel [ > > ]
	Lambert, Philippe [Université de Liège - ULg > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales >]
Publication date :	2001
Journal title :	Proceedings of the Casualty Actuarial Society
Volume :	LXXXVIII
Pages :	142-174
Audience :	International
Abstract :	[en] Mixed Poisson distributions are widely used for modeling claim counts when the portfolio is thought to be heterogeneous. The risk (or mixing) distribution then represents a measure of this heterogeneity. The aim of this paper is to use a variant of the Patilea and Rolin [15] smoothed version of the Simar [20] Non-Parametric Maximum Likelihood Estimator of the risk distribution in the mixed Poisson model. Empirical results based on two data sets from automobile third-party liability insurance demonstrate the relevance of this approach. The design of merit-rating schemes is discussed in the second part of the paper.
Permalink :	http://hdl.handle.net/2268/24517
Other URL :	http://www.casact.org/pubs/proceed/proceed01/01142.pdf

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