PARAMETRIC INFERENCE IN MULTIVARIATE MARKOV MODELS WITH DEPENDENCE ON COVARIATES

Manuel MOLINA FERNANDEZ1

Miguel GONZALEZ VELASCO[1]

Manuel BARRANTES LOPEZ[2]

§    ABSTRACT: In this paper, the modelization of the probability distribution of a nominal-scaled response vector, influenced by covariates, is studied. We assume that the sequence of responses is well described by a multivariate Markov chain and we model their transition probabilities by mean of the multiple-group logistic regression model. The maximum likelihood estimation of the regression parameters is considered and a recursive method to estimate the parameters when some responses are missing is suggested. Finally, the likelihood ratio criterion is used to test some hypotheses about the model.

§    KEYWORDS: Multivariate Markov chain; multiple-group logistic regression model; maximum likelihood inference.

 

 



[1] Departaments of Mathematics.

[2] Departament of Didatic of the Experimental Sciences and the Mathematics - University of Extremadura - 06071 - Badajoz - Spain..