1k citations · 1.6k across the 56 of their papers we have counts for
6 papers · 1 filter
A deconvolution path for mixtures
Oscar Hernan Madrid Padilla, Nicholas G. Polson, James G. Scott
We propose a class of estimators for deconvolution in mixture models based on a simple two-step "bin-and-smooth" procedure applied to histogram counts. The method is both statistic…
Why Indexing Works
J. B. Heaton, N. G. Polson, J. H. Witte
We develop a simple stock selection model to explain why active equity managers tend to underperform a benchmark index. We motivate our model with the empirical observation that th…
Default Bayesian analysis with global-local shrinkage priors
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
We provide a framework for assessing the default nature of a prior distribution using the property of regular variation, which we study for global-local shrinkage priors. In partic…
A Statistical Theory of Deep Learning via Proximal Splitting
Nicholas G. Polson, Brandon T. Willard, Massoud Heidari
In this paper we develop a statistical theory and an implementation of deep learning models. We show that an elegant variable splitting scheme for the alternating direction method…
Proximal Algorithms in Statistics and Machine Learning
Nicholas G. Polson, James G. Scott, Brandon T. Willard
In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for…
The Horseshoe+ Estimator of Ultra-Sparse Signals
Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson +1
We propose a new prior for ultra-sparse signal detection that we term the "horseshoe+ prior." The horseshoe+ prior is a natural extension of the horseshoe prior that has achieved s…