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.
Type
Publication
ASTIN Bulletin
Andrei Badescu
Professor, Actuarial Science; Director, Master of Financial Insurance
