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stat.ME2026
Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications
Anant Mathur, Benoit Liquet, Samuel Muller +1
High-dimensional biomedical studies require models that are simultaneously accurate, sparse, and interpretable, yet exact best subset selection for generalized linear models is com…
stat.ME2024
Group COMBSS: Group Selection via Continuous Optimization
Anant Mathur, Sarat Moka, Benoit Liquet +1
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is…
stat.ME2023
Generalized Linear Models via the Lasso: To Scale or Not to Scale?
Anant Mathur, Sarat Moka, Zdravko Botev
The Lasso regression is a popular regularization method for feature selection in statistics. Prior to computing the Lasso estimator in both linear and generalized linear models, it…