The likelihood for copula modeling appears when both the data and the copula representations are seen as being driven by common uniform latent variables. This perspective facilitates Bayesian inference for prediction and copula selection.

We develop methodology that combines statistical learning methods with generalized Markov models, thereby enhancing the former to account for time series dependence. Our methodology can accommodate very general and very long-term time dependence structures in an easily estimable and...

A major challenge for the treatment of human immunodeficiency virus (HIV) infection is the development of therapy-resistant strains. We present a statistical model that quantifies the evolution of HIV populations when exposed to particular therapies. A hierarchical Bayesian approach is used to...

Recent research has documented a significant rise in the volatility (e.g., expected squared change) of individual incomes in the U.S. since the 1970s. Existing measures of this trend abstract from individual heterogeneity, effectively estimating an increase in average volatility. We decompose...

Despite rapid developments in stochastic search algorithms, the practicality of Bayesian variable selection methods has continued to pose challenges. High-dimensional data are now routinely analyzed, typically with many more covariates than observations. To broaden the applicability of Bayesian...

Averaged orthogonal rotations of Zellner's g-prior yield general, interpretable, closed form Bayes factors for the normal linear model variable selection problem. Coupled with a model space prior that balances the weight between the identifiable and the unidentifiable models, limiting forms for...