Data Augmentation and MCMC for Binary and Multinomial Logit Models
Sprache des Titels:
Englisch
Original Kurzfassung:
The paper introduces two new data augmentation algorithms for sampling the parameters of a binary or multinomial logit model from their posterior distribution within a Bayesian framework. The new samplers are based on rewriting the underlying random utility model in such a way that only differences of utilities are involved. As a conseqence, the error term in the logit model has a logistic
distribution. If the logistic distribution is approximated by a finite scale mixture of normal distributions, auxiliary mixture sampling can be implemented to sample from the posterior of the regression parameters. ternatively, a data
augmented Metropolis–Hastings algorithm can be formulated by approximating
the logistic distribution by a single normal distribution. A comparative study on
five binomial and multinomial data sets shows that the new samplers are superior
to other data augmentation samplers and to Metropolis–Hastings sampling
without data augmentation.
Forschungs-