58 citations · 172 across the 16 of their papers we have counts for
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An empirical -Wishart prior for sparse high-dimensional Gaussian graphical models
Chang Liu, Ryan Martin
In Gaussian graphical models, the zero entries in the precision matrix determine the dependence structure, so estimating that sparse precision matrix and, thereby, learning this un…
Model-free posterior inference on the area under the receiver operating characteristic curve
Zhe Wang, Ryan Martin
The area under the receiver operating characteristic curve (AUC) serves as a summary of a binary classifier's performance. Methods for estimating the AUC have been developed under…
Permutation-based uncertainty quantification about a mixing distribution
Vaidehi Dixit, Ryan Martin
Nonparametric estimation of a mixing distribution based on data coming from a mixture model is a challenging problem. Beyond estimation, there is interest in uncertainty quantifica…
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…
Empirical priors for prediction in sparse high-dimensional linear regression
Ryan Martin, Yiqi Tang
In this paper we adopt the familiar sparse, high-dimensional linear regression model and focus on the important but often overlooked task of prediction. In particular, we consider…