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EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION

Published online by Cambridge University Press:  13 May 2016

Cuihong Yin  and
X. Sheldon Lin
Cuihong Yin
Affiliation:
School of Mathematical Sciences, Xiamen University, Xiamen, China E-Mail: 460279857@qq.com
X. Sheldon Lin*
Affiliation:
School of Mathematical Sciences, Xiamen University, Xiamen, China Department of Statistical Sciences, University of Toronto, Toronto, Ontario, CanadaM5S 3G3
*
E-Mail: sheldon@utstat.utoronto.ca
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Abstract

The Erlang mixture model has been widely used in modeling insurance losses due to its desirable distributional properties. In this paper, we consider the problem of efficient estimation of the Erlang mixture model. We present a new thresholding penalty function and a corresponding EM algorithm to estimate model parameters and to determine the order of the mixture. Using simulation studies and a real data application, we demonstrate the efficiency of the EM algorithm.

Keywords

Erlang mixtureEM algorithmiSCAD penaltyVaRTVaR
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 46 , Issue 3 , September 2016 , pp. 779 - 799
Copyright
Copyright © Astin Bulletin 2016 

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