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FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM

Published online by Cambridge University Press:  20 July 2015

Roel Verbelen ,
Lan Gong ,
Katrien Antonio ,
Andrei Badescu  and
Sheldon Lin
Roel Verbelen*
Affiliation:
LStat, Faculty of Economics and Business, KU Leuven, Belgium
Lan Gong
Affiliation:
Department of Statistical Sciences, University of Toronto, Canada E-Mail: lan.gong@tdsecurities.com
Katrien Antonio
Affiliation:
LStat, Faculty of Economics and Business, KU Leuven, Belgium Faculty of Economics and Business, University of Amsterdam, the Netherlands E-Mail: Katrien.Antonio@kuleuven.be
Andrei Badescu
Affiliation:
Department of Statistical Sciences, University of Toronto, Canada E-Mail: badescu@utstat.toronto.edu
Sheldon Lin
Affiliation:
Department of Statistical Sciences, University of Toronto, Canada E-Mail: sheldon@utstat.toronto.edu
*
E-Mail: roel.verbelen@kuleuven.be
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Abstract

We discuss how to fit mixtures of Erlangs to censored and truncated data by iteratively using the EM algorithm. Mixtures of Erlangs form a very versatile, yet analytically tractable, class of distributions making them suitable for loss modeling purposes. The effectiveness of the proposed algorithm is demonstrated on simulated data as well as real data sets.

Keywords

Mixture of Erlang distributions with a common scale parametercensoringtruncationexpectation–maximization algorithmmaximum likelihood

Information

Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 45 , Issue 3 , September 2015 , pp. 729 - 758
Copyright
Copyright © Astin Bulletin 2015 

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