600 citations · 807 across the 35 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…
Nonparametric density estimation by histogram trend filtering
Oscar Hernan Madrid Padilla, James G. Scott
We propose a novel approach for density estimation called histogram trend filtering. Our estimator arises from looking at surrogate Poisson model for counts of observations in a pa…
Multiscale spatial density smoothing: an application to large-scale radiological survey and anomaly detection
Wesley Tansey, Alex Athey, Alex Reinhart +1
We consider the problem of estimating a spatially varying density function, motivated by problems that arise in large-scale radiological survey and anomaly detection. In this conte…
A Fast and Flexible Algorithm for the Graph-Fused Lasso
Wesley Tansey, James G. Scott
We propose a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constan…
Tensor decomposition with generalized lasso penalties
Oscar Hernan Madrid Padilla, James G. Scott
We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multi-way data. This generalizes existing work on sparse tensor de…
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…