Search Results for author:
Found 3 papers, 0 papers with code
Efficiently handling constraints with Metropolis-adjusted Langevin algorithm
In this study, we investigate the performance of the Metropolis-adjusted Langevin algorithm in a setting with constraints on the support of the target distribution.
Pre-processing with Orthogonal Decompositions for High-dimensional Explanatory Variables
Strong correlations between explanatory variables are problematic for high-dimensional regularized regression methods.
Tuning parameter selection in high dimensional penalized likelihood
We examine this problem in the setting of penalized likelihood methods for generalized linear models, where the dimensionality of covariates p is allowed to increase exponentially with the sample size n. We propose to select the tuning parameter by optimizing the generalized information criterion (GIC) with an appropriate model complexity penalty.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
