Title

Model-based Clustering of Count Processes

Publication Name

Journal of Classification

Abstract

A model-based clustering method based on Gaussian Cox process is proposed to address the problem of clustering of count process data. The model allows for nonparametric estimation of intensity functions of Poisson processes, while simultaneous clustering count process observations. A logistic Gaussian process transformation is imposed on the intensity functions to enforce smoothness. Maximum likelihood parameter estimation is carried out via the EM algorithm, while model selection is addressed using a cross-validated likelihood approach. The proposed model and methodology are applied to two datasets.

Open Access Status

This publication is not available as open access

Volume

38

Issue

2

First Page

188

Last Page

211

Funding Number

SFI/12/RC/2289

Funding Sponsor

Science Foundation Ireland

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Link to publisher version (DOI)

http://dx.doi.org/10.1007/s00357-020-09363-4