Add to ORKGThis paper is concerned with statistical inference in multinomial probit, multinomial-t and multinomial logit models. New Markov chain Monte Carlo (MCMC) algorithms for tting these models are introduced and compared with existing MCMC methods. The question of parameter identication in the multinomial probit model is readdressed. Model comparison issues are also discussed and the method of Chib (1995) is utilized to nd Bayes factors for competing multinomial probit and multinomial logit models. The methods and ideas are illustrated in detail with an example
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) es...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...
This paper is concerned with statistical inference in multinomial probit, multinomial-$t$ and multin...
A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ...
A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ...
This research compares several approaches to inference in the multinomial probit model, based on two...
Abstract-This research compares several approaches to in-ference in the multinomial probit model, ba...
We develop Metropolis-Hastings algorithms for exact conditional inference, including goodness-of-fit...
AbstractThis paper studies a Metropolis-Hastings (MH) algorithm of unknown parameters for a multinom...
Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient met...
Multinomial logistic regression is one of the most popular models for modelling the effect of explan...
The multinomial logit model (MNL) possesses a latent variable representation in terms of random var...
A rule of thumb is suggested for comparing multinomial logit coefficients with multinomial probit co...
A rule of thumb is suggested for comparing multinomial logit coefficients with multinomial probit co...
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) es...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...
This paper is concerned with statistical inference in multinomial probit, multinomial-$t$ and multin...
A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ...
A Bayesian approach to estimation of the regression coefficients of a multinominal logit model with ...
This research compares several approaches to inference in the multinomial probit model, based on two...
Abstract-This research compares several approaches to in-ference in the multinomial probit model, ba...
We develop Metropolis-Hastings algorithms for exact conditional inference, including goodness-of-fit...
AbstractThis paper studies a Metropolis-Hastings (MH) algorithm of unknown parameters for a multinom...
Multivariate ordinal data arise in many areas of applications. This paper proposes new efficient met...
Multinomial logistic regression is one of the most popular models for modelling the effect of explan...
The multinomial logit model (MNL) possesses a latent variable representation in terms of random var...
A rule of thumb is suggested for comparing multinomial logit coefficients with multinomial probit co...
A rule of thumb is suggested for comparing multinomial logit coefficients with multinomial probit co...
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) es...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...
We outline a new estimation method for the multinomial probit model (MNP). The method is a different...