On the Class of Erlang Mixtures with Risk Theoretic Applications

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Hypoexponential Distribution with Different Parameters (2013, background): “In particular the sum of exponential random has important applications in the modeling in many domains such as communications and computer science [3,4], Markov process [5,6], insurance [7,8] and reliability and performance evaluation [4,5,9, 10].”
FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM (2015, methodology): “Klugman et al. (2013), Willmot and Lin (2011) and Willmot and Woo (2007) give an overview of these analytical and computational properties of mixtures of Erlangs.”
Modeling Dependent Risks with Multivariate Erlang Mixtures (2009, background): “They follows from 5.1 and the properties of a univariate Erlang mixture (see, for example, Willmot and Woo (2007) or Lee and Lin (2010)).”
Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions (2010, methodology): “The use of mixtures of Erlang distributions for individual insurance losses has been discussed and advocated in several papers, including Lin and Willmot (2000), Willmot and Lin (2001), Dickson and Willmot (2005), Willmot and Woo (2007), and Lin and Wang (2008). Many quantities of interest in loss a”
TVaR-based capital allocation for multivariate compound distributions with positive continuous claim amounts (2012, background): “Willmot and Woo (2007), Lee and Lin (2010), and Willmot and Lin (2011) show the usefulness of this distribution.”