Nonparametric Shrinkage Estimation in High Dimensional Glms via Polya Trees

Regularization in fitting regression models has been a very active topic of research in the past few decades, but most of the existing methods are designed for particular situations, e.g. for the case of a sparse coefficient vector.We consider the problem of designing universally optimal regularized estimators in a given generalized linear model with fixed effects.First, we propose as a contender the Bayes estimator against an ideal prior that assigns equal mass to every permutation of the fixed