Biclustering multivariate discrete longitudinal data

A model-based biclustering method for multivariate discrete longitudinal data is proposed. We consider a finite mixture of generalized linear models to cluster units and, within each mixture component, we adopt a flexible and parsimonious parameterization of the component-specific canonical paramete...

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Bibliographic Details
Published in:Statistics and computing 2024-02, Vol.34 (1), Article 42
Main Authors: Alfó, M., Marino, M. F., Martella, F.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
Crime
Maximum likelihood estimation
Mixtures
Multivariate analysis
Original Paper
Parameterization
Parameters
Probability and Statistics in Computer Science
Segments
Statistical models
Statistical Theory and Methods
Statistics and Computing/Statistics Programs
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Summary:A model-based biclustering method for multivariate discrete longitudinal data is proposed. We consider a finite mixture of generalized linear models to cluster units and, within each mixture component, we adopt a flexible and parsimonious parameterization of the component-specific canonical parameter to define subsets of variables (segments) sharing common dynamics over time. We develop an Expectation-Maximization-type algorithm for maximum likelihood estimation of model parameters. The performance of the proposed model is evaluated on a large scale simulation study, where we consider different choices for the sample the size, the number of measurement occasions, the number of components and segments. The proposal is applied to Italian crime data (font ISTAT) with the aim to detect areas sharing common longitudinal trajectories for specific subsets of crime types. The identification of such biclusters may potentially be helpful for policymakers to make decisions on safety.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-023-10292-6