Time-varying clustering of multivariate longitudinal observations

Antonello Maruotti and Maurizio Vichi

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 2, 430-443

Abstract: We propose a statistical method for clustering multivariate longitudinal data into homogeneous groups. This method relies on a time-varying extension of the classical K-means algorithm, where a multivariate vector autoregressive model is additionally assumed for modeling the evolution of clusters' centroids over time. Model inference is based on a least-squares method and on a 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.

Date: 2016
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DOI: 10.1080/03610926.2013.821488

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