Sampling from the posterior distribution in generalized linear mixed models
Statistics and Computing
On convergence of the EM algorithmand the Gibbs sampler
Statistics and Computing
Computational Statistics & Data Analysis
Block sampler and posterior mode estimation for asymmetric stochastic volatility models
Computational Statistics & Data Analysis
Forecasting binary longitudinal data by a functional PC-ARIMA model
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling
Computational Statistics & Data Analysis
Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data
Statistics and Computing
Bayesian estimation of random effects models for multivariate responses of mixed data
Computational Statistics & Data Analysis
A Bayesian approach to model-based clustering for binary panel probit models
Computational Statistics & Data Analysis
Bayesian estimation and stochastic model specification search for dynamic survival models
Statistics and Computing
Computational Statistics & Data Analysis
Efficient MCMC for Binomial Logit Models
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Bayesian dynamic probit models for the analysis of longitudinal data
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A new method of data augmentation for binary and multinomial logit models is described. First, the latent utilities are introduced as auxiliary latent variables, leading to a latent model which is linear in the unknown parameters, but involves errors from the type I extreme value distribution. Second, for each error term the density of this distribution is approximated by a mixture of normal distributions, and the component indicators in these mixtures are introduced as further latent variables. This leads to Markov chain Monte Carlo estimation based on a convenient auxiliary mixture sampler that draws from standard distributions like normal or exponential distributions and, in contrast to more common Metropolis-Hastings approaches, does not require any tuning. It is shown how the auxiliary mixture sampler is implemented for binary or multinomial logit models, and it is demonstrated how to extend the sampler to mixed effect models and time-varying parameter models for binary and categorical data. Finally, an application to Austrian labor market data is discussed.