153 citations · 160 across the 5 of their papers we have counts for
6 papers
Global Inference for High Dimensional Quantile Regression with Heterogeneity
Tate Jacobson, Daeyoung Ham, Ben Sherwood
Quantile regression has received considerable attention in high dimensional methodology due to its robustness and ability to accommodate heteroskedasticity. Despite this attention,…
On the Use of Minimum Penalties in Statistical Learning
Ben Sherwood, Bradley S. Price
Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. Th…
Estimating Multiple Precision Matrices with Cluster Fusion Regularization
Bradley S. Price, Aaron J. Molstad, Ben Sherwood
We propose a penalized likelihood framework for estimating multiple precision matrices from different classes. Most existing methods either incorporate no information on relationsh…
A Cluster Elastic Net for Multivariate Regression
Bradley S. Price, Ben Sherwood
We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion pen…
Partially linear additive quantile regression in ultra-high dimension
Ben Sherwood, Lan Wang
We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering dif…
Variable Selection for Additive Partial Linear Quantile Regression with Missing Covariates
Ben Sherwood
The standard quantile regression model assumes a linear relationship at the quantile of interest and that all variables are observed. We relax these assumptions by considering a pa…