Time-varying clustering of multivariate longitudinal observations

2014/04/24 by Antonello Maruotti, Maruotti, Antonello, Maurizio Vichi +1
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1404.6201

openalex publication_date 2014/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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