14 citations · 30 across the 6 of their papers we have counts for
6 papers
Response-guided knockoffs for directional FDR control in linear models
Jack Freestone, Garth Tarr, Samuel Muller +1
We consider the problem of feature selection in linear models with finite-sample control of the false discovery rate (FDR). While existing knockoff-based methods control the direct…
Robust Best Subset Selection via Fast Approximate MM-Estimation
Martin Huang, Samuel Muller, Garth Tarr
Best subset selection procedures typically rely on a squared error loss, where a small number of outlying observations may distort the entire solution path. Replacing this loss wit…
CR-Lasso: Robust cellwise regularized sparse regression
Peng Su, Garth Tarr, Samuel Muller +1
Cellwise contamination remains a challenging problem for data scientists, particularly in research fields that require the selection of sparse features. Traditional robust methods…
Robust Variable Selection under Cellwise Contamination
Peng Su, Garth Tarr, Samuel Muller
Cellwise outliers are widespread in data and traditional robust methods may fail when applied to datasets under such contamination. We propose a variable selection procedure, that…
Semiparametric Regression using Variational Approximations
Francis K. C. Hui, Chong You, Han Lin Shang +1
Semiparametric regression offers a flexible framework for modeling non-linear relationships between a response and covariates. A prime example are generalized additive models where…
mplot: An R Package for Graphical Model Stability and Variable Selection Procedures
Garth Tarr, Samuel Müller, Alan Welsh
The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Müller and Welsh 2010; Murray, Heritier, and Müller 2013) as well as the a…