Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions

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Multivariate distribution defined with Farlie–Gumbel–Morgenstern copula and mixed Erlang marginals: Aggregation and capital allocation (2013, unknown): “Lee and Lin (2010) suggest the use of mixed Erlang distributions to model insurance losses.”
Modeling loss data using mixtures of distributions (2016, methodology): “Lee and Lin (2010) showed that Log-normal, Gamma, and GeneralizedPareto densities can be suitably approximated with these Erlang mixtures, and they also demonstrated their proposed approach on catastrophic loss data from the United States.”
TVaR-based capital allocation for multivariate compound distributions with positive continuous claim amounts (2012, methodology): “Willmot and Woo (2007), Lee and Lin (2010), and Willmot and Lin (2011) show the usefulness of this distribution. With several examples, these authors illustrate the versatility of this distribution to model claim amounts and the feasibility to obtain closedform expressions for various quantities of ”
FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM (2015, methodology): “Extending Lee and Lin (2010) 2, we introduce a 2We acknowledge the help of Simon Lee who suggested this approach in personal communication. spread factor s by which we multiply the shapes in order to get a wider spread at the initial step, i.e. rj = sj for j = 1, 2, . . . ,M .”