An infinite mixture of autoregressive models is developed. The unknown parameters in the mixture autoregressive model follow a mixture distribution, which is governed by a Dirichlet process prior. One main feature of our approach is the generalization of a finite mixture model by having the...

This paper develops nonparametric estimation for discrete choice models based on the Mixed Multinomial Logit (MMNL) model. It has been shown that MMNL models encompass all discrete choice models derived under the assumption of random utility maximization, subject to the identification of an...

This paper presents a Bayesian semi-parametric approach to mixed proportional hazard models. The unobserved heterogeneity and the baseline hazard rate are assumed to be an integrated Dirichlet process and an integrated weighted gamma process, respectively. Bayesian estimators and a numerical...