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Time-varying clustering of multivariate longitudinal observations

Author

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  • Antonello Maruotti
  • Maurizio Vichi

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.

Suggested Citation

  • Antonello Maruotti & Maurizio Vichi, 2016. "Time-varying clustering of multivariate longitudinal observations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(2), pages 430-443, January.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:2:p:430-443
    DOI: 10.1080/03610926.2013.821488
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    Cited by:

    1. Benjamin Auder & Jairo Cugliari & Yannig Goude & Jean-Michel Poggi, 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting," Energies, MDPI, vol. 11(7), pages 1-22, July.

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