61 citations · 92 across the 11 of their papers we have counts for
11 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…
Outlier detection in state-space models using mean-shift penalisation
Rajan Shankar, Ines Wilms, Jakob Raymaekers +1
State-space models (SSMs) provide a flexible framework for modelling time series data, but their reliance on Gaussian error assumptions makes them highly sensitive to outliers. We…
The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression
Mohammad Javad Davoudabadi, Jonathon Tidswell, Samuel Muller +2
In this paper, we introduce a new probability distribution, the Lasso distribution. We derive several fundamental properties of the distribution, including closed-form expressions…
Data-Adaptive Automatic Threshold Calibration for Stability Selection
Martin Huang, Samuel Muller, Garth Tarr
Stability selection has gained popularity as a method for enhancing the performance of variable selection algorithms while controlling false discovery rates. However, achieving the…
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…
Machine learning applications in time series hierarchical forecasting
Mahdi Abolghasemi, Rob J Hyndman, Garth Tarr +1
Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a de…
Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion
Mahdi Abolghasemi, Richard Gerlach, Garth Tarr +1
The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Demand unpredictability…
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…
Robust estimation of precision matrices under cellwise contamination
Garth Tarr, Samuel Müller, Neville C. Weber
There is a great need for robust techniques in data mining and machine learning contexts where many standard techniques such as principal component analysis and linear discriminant…