Fitting mixtures of Erlangs to censored and truncated data using the EM algorithm

Authors
  • S. Lin
Publication date 2015
Journal ASTIN Bulletin
Volume | Issue number 45 | 3
Pages (from-to) 729-758
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam School of Economics Research Institute (ASE-RI)
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.
Document type Article
Language English
Published at
https://doi.org/10.1017/asb.2015.15 (Final published version)
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