Multivariate mixtures of Erlangs for density estimation under censoring

Roel Verbelen (), Katrien Antonio and Gerda Claeskens ()
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Roel Verbelen: KU Leuven
Gerda Claeskens: KU Leuven

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 3, No 6, 429-455

Abstract: Abstract 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.

Keywords: Multivariate mixtures of Erlangs with a common scale parameter; Density estimation; Censored data; Expectation–maximization algorithm; Maximum likelihood (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-015-9343-y

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