Fitting Mixtures of Erlangs to Censored and Truncated Data Using the EM Algorithm


We present a calibration procedure for fitting 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 modeling purposes. The effectiveness of the proposed algorithm is demonstrated on simulated data as well as real data sets.

Astin Bulletin, 45(3), 729–758.
Roel Verbelen

My research interests include statistics, machine learning, general insurance and rstats.