8 citations · 14 across the 4 of their papers we have counts for
4 papers
Expectation-Propagation for Bayesian Empirical Likelihood Inference
Kenyon Ng, Weichang Yu, Howard D. Bondell
Bayesian inference typically relies on specifying a parametric model that approximates the data-generating process. However, misspecified models can yield poor convergence rates an…
Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm
Alexander C. McLain, Anja Zgodic, Howard Bondell
Bayesian variable selection methods are powerful techniques for fitting and inferring on sparse high-dimensional linear regression models. However, many are computationally intensi…
Variational approximations using Fisher divergence
Yue Yang, Ryan Martin, Howard Bondell
Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The…
Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior
Chang Liu, Yue Yang, Howard Bondell +1
In the context of a high-dimensional linear regression model, we propose the use of an empirical correlation-adaptive prior that makes use of information in the observed predictor…