In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. We …

In this paper we consider regression models for count data allowing for overdispersion in a Bayesian framework. Besides … autoregressive prior based on Pettitt et al. (2002). Apart from the Poisson regression model, the negative binomial and the … generalized Poisson regression model are addressed. Further, zero-inflated models combined with the Poisson and generalized …

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large … Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for …

This paper revisits a number of data-rich prediction methods, like factor models, Bayesian ridge regression and … method: partial least squares regression. Under the latter, linear, orthogonal combinations of a large number of predictor …. We also argue that forecast combinations can be interpreted as a restricted form of partial least squares regression …

known alternative: partial least squares (PLS) regression. In this method, linear, orthogonal combinations of a large number … maximized. We show theoretically that when the data have a factor structure, PLS regression can be seen as an alternative way to … possibly vanishes in the limit, PLS regression still provides asymptotically the best fit for the target variable of interest …

Recent work by Schennach (2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called …

Recent work by Schennach(2005) has opened the way to a Bayesian treatment of quantile regression. Her method, called …