Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Ba...
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Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A markov-chainmontecarlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.
We describe a Bayesian hierarchical model to analyze autoregressive time series panel data. We develop two algorithms using markov-chainmontecarlo methods, a restricted algorithm that enforces stationarity or nonsta...
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We describe a Bayesian hierarchical model to analyze autoregressive time series panel data. We develop two algorithms using markov-chainmontecarlo methods, a restricted algorithm that enforces stationarity or nonstationarity conditions on the series and an unrestricted algorithm that does not, Two examples show that restricting stationary series to be stationary provides no new information, but restricting nonstationary series to be stationary leads to substantial differences from the unrestricted case. These examples and a simulation study also show that, compared with inference based on individual series, there are gains in precision for estimation and forecasting when similar series are pooled.
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