DMRnet - Delete or Merge Regressors Algorithms for Linear and Logistic
Model Selection and High-Dimensional Data
Model selection algorithms for regression and
classification, where the predictors can be continuous or
categorical and the number of regressors may exceed the number
of observations. The selected model consists of a subset of
numerical regressors and partitions of levels of factors.
Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and
Agnieszka Sołtys, 2023. Improving Group Lasso for
High-Dimensional Categorical Data. In: Computational Science –
ICCS 2023. Lecture Notes in Computer Science, vol 14074, p.
455-470. Springer, Cham. <doi:10.1007/978-3-031-36021-3_47>.
Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka
Prochenka, 2015. Delete or merge regressors for linear model
selection. Electronic Journal of Statistics 9(2): 1749-1778.
<doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk,
2015. Combined l1 and greedy l0 penalized least squares for
linear model selection. Journal of Machine Learning Research
16(29): 961-992.
<https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>.
Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał
Frej and Jan Mielniczuk, 2022. Improving Lasso for model
selection and prediction. Scandinavian Journal of Statistics,
49(2): 831–863. <doi:10.1111/sjos.12546>.
