Time-varying clustering of multivariate longitudinal observations
Methodology
2014-04-25 v1
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.
Cite
@article{arxiv.1404.6201,
title = {Time-varying clustering of multivariate longitudinal observations},
author = {Antonello Maruotti and Maurizio Vichi},
journal= {arXiv preprint arXiv:1404.6201},
year = {2014}
}