Mixture of Erlangs

Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions

We develop a general procedure to fit a splicing model to censored and/or truncated data. We propose to combine the flexibility of the mixed Erlang distribution to model the body of the distribution with the Pareto distribution to provide a suitable fit for the tail.

Data analytics for insurance loss modelling, telematics pricing and claims reserving.

PhD Thesis

Multivariate mixtures of Erlangs for density estimation under censoring.

We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, that is able to deal with randomly censored and fixed truncated data.

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.

Loss modeling using mixtures of Erlangs

We demonstrate our implemented fitting procedure and graphical tools built in R for univariate and multivariate mixtures of Erlangs in several applications in the context of loss modelling.