Modelling of Correlated Ordinal Responses, by Using Multivariate Skew Probit with Different Types of Variance Covariance Structures

In this paper, a multivariate fundamental skew probit (MFSP) model is used to model correlated ordinal responses which are constructed from the multivariate fundamental skew normal (MFSN) distribution originate to the greater flexibility of MFSN. To achieve an appropriate VC structure for reaching reliable statistical inferences, many types of variance covariance (VC) structures are considered to model MFSN. Simulation methods are used to find the properties of the parameters estimate. The Schizophrenia Collaborative Study data invokes the proposed MFSN model. The results confirm that the first-order autoregressive (AR(1)) structure substantially enhances the estimation of the parameters. Furthermore, over time the drugs effect the schizophrenia treatment, noticeably.