Markov Switching Copula Models for Longitudinal Data

Abstract

In this paper we present a novel Markov Switching generative model for continuous multivariate time series and longitudinal data based on Gaussian copula functions. We assume that the values of the multivariate time series at every time slice are sampled out of a joint probability distribution that is selected by the latent state. The use of Gaussian copula functions give the flexibility of individual marginals for each time series and a common dependence structure given by a correlation matrix. The transition matrix together with all the observation models are learned by means of Gibbs sampling. We also test the method both with synthetic and real data sets, and compare them with other usual techniques. Results show that models assuming normality in real data sets are not a good approach when marginals are not Gaussian, and they are outranked by our proposal.

DOI: 10.1109/ICDMW.2016.0159

Cite this paper

@article{CuestaInfante2016MarkovSC, title={Markov Switching Copula Models for Longitudinal Data}, author={Alfredo Cuesta-Infante and Kalyan Veeramachaneni}, journal={2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)}, year={2016}, pages={1104-1109} }