Time-varying clustering of multivariate longitudinal observations

Antonello Maruotti; Maurizio Vichi

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arXiv · 1404.6201

Time-varying clustering of multivariate longitudinal observations

Antonello Maruotti·Maurizio Vichi

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Abstract

We propose a statistical method for clustering of multivariate longitudinal data into homogeneous groups. This method relies on a time-varying extension on the classical K-means algorithm, where a multivariate vector autoregressive model is additionally assumed for modeling the evolution of clusters' centroids over time. We base the inference on a least squares specification of the model and coordinate descent algorithm. To illustrate our work, we consider a longitudinal dataset on human development. Three variables are modeled, namely life expectancy, education and gross domestic product.

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Antonello Maruotti, Maurizio Vichi. 2014-04-24. Time-varying clustering of multivariate longitudinal observations. https://arxiv.org/abs/1404.6201

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