In this paper, we show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by testing hypotheses of Granger non-causality, contemporaneous independence and monotone dependence coherent with a stochastic ordering. The tested hypotheses associated to a multi edge graph are proven to be equivalent to equality and inequality constraints on interactions of a multivariate logistic model parameterizing the transition probabilities. As the null hypothesis is specified by inequality constraints, the likelihood ratio statistic has chi-bar-square asymptotic distribution whose tail probabilities can be computed by simulation. The introduced hypotheses are tested on real categorical time series. © Springer-Verlag Berlin Heidelberg 2010.
CITATION STYLE
Colombi, R., & Giordano, S. (2010). Monotone graphical multivariate Markov chains. In Proceedings of COMPSTAT 2010 - 19th International Conference on Computational Statistics, Keynote, Invited and Contributed Papers (pp. 445–452). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-7908-2604-3_43
Mendeley helps you to discover research relevant for your work.