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 randomly censored and fixed truncated data. The effectiveness of the proposed algorithm is demonstrated on simulated as well as real data sets.
Faculty of Economics and Business, Leuven Statistics Research Center, KU Leuven, Leuven, Belgium. roel.verbelen@kuleuven.be.
Roel
0000-0002-2347-9240
R
Verbelen
Faculty of Economics and Business, Leuven Statistics Research Center, KU Leuven, Leuven, Belgium.|Faculty of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands.
Katrien
K
Antonio
Faculty of Economics and Business, Leuven Statistics Research Center, KU Leuven, Leuven, Belgium.
Gerda
G
Claeskens
D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D015999:Multivariate Analysis
Censored data; Density estimation; Expectation–maximization algorithm; Maximum likelihood; Multivariate mixtures of Erlangs with a common scale parameter