1 citations · 2 across the 3 of their papers we have counts for
3 papers
Improving Group Lasso for high-dimensional categorical data
Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel +1
Sparse modelling or model selection with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or…
Group Lasso merger for sparse prediction with high-dimensional categorical data
Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel
Sparse prediction with categorical data is challenging even for a moderate number of variables, because one parameter is roughly needed to encode one category or level. The Group L…
Linear regression model selection using p-values when the model dimension grows
Piotr Pokarowski, Jan Mielniczuk, Paweł Teisseyre
We consider a new criterion-based approach to model selection in linear regression. Properties of selection criteria based on p-values of a likelihood ratio statistic are studied f…