On the estimation of mixtures of Poisson regression models with large number of components

Panagiotis Papastamoulis, Marie-Laure Martin-Magniette and Cathy Maugis-Rabusseau

Computational Statistics & Data Analysis, 2016, vol. 93, issue C, 97-106

Abstract: Modelling heterogeneity in large datasets of counts under the presence of covariates demands advanced clustering methods. Towards this direction a mixture of Poisson regressions is proposed. Conditionally on the covariates and a cluster, the multivariate distribution is a product of independent Poisson distributions. A variety of different parameterizations is taken into account for the slope of the conditional log-means. Also considered is the case of partitioning the response variables into sets of replicates sharing the same conditional log-mean up to an additive constant. Model parameters are estimated via an Expectation–Maximization algorithm with Newton–Raphson steps. In particular, an efficient initialization is introduced in order to improve the inference: a splitting scheme is combined with a Small-EM strategy. Simulations and application on two real high-throughput sequencing datasets highlight improvements of parameter estimations. The proposed methodology is implemented in the R package poisson.glm.mix, available on CRAN.

Keywords: Mixtures of distributions; EM algorithm initialization; Multimodal likelihood; Clustering (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:93:y:2016:i:c:p:97-106

DOI: 10.1016/j.csda.2014.07.005

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