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

By Roel Verbelen, Lan Gong, Katrien Antonio, Andrei L. Badescu, Sheldon Lin. ASTIN Bulletin: The Journal of the International Actuarial Association, 2015

77 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/10c39aebdb27b3586a06ce898b1074aee3c4f5ad

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In Artificial Intelligence

1. Tatjana Miljkovic
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1. Tatjana Miljkovic
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Citations in the corpus

• Modeling loss data using mixtures of distributions (2016, methodology): “Notable exceptions are Lee and Lin (2010) and Verbelen et al. (2015, 2016) who consider finite mixtures of ∗ Corresponding author.”