Multivariate mixtures of Erlangs for density estimation under censoring

Multivariate mixtures of Erlang distributions form a versatile, yet analytically tractable, class of distributions making them suitable for multivariate density estimation. We present a flexible and effective fitting procedure for multivariate mixtures of Erlangs, which iteratively uses the EM algorithm, by introducing a computationally efficient initialization and adjustment strategy for the shape parameter vectors. We furthermore extend the EM algorithm for multivariate mixtures of Erlangs to be able to deal with censored and truncated data. The effectiveness of the proposed algorithm, which has been implemented in R, is demonstrated on simulated as well as real data sets.nrpages: 34status: publishe