13 citations · 21 across the 5 of their papers we have counts for
6 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…
Improving Lasso for model selection and prediction
Piotr Pokarowski, Wojciech Rejchel, Agnieszka Soltys +2
It is known that the Thresholded Lasso (TL), SCAD or MCP correct intrinsic estimation bias of the Lasso. In this paper we propose an alternative method of improving the Lasso for p…
Delete or merge regressors for linear model selection
Aleksandra Maj-Kańska, Piotr Pokarowski, Agnieszka Prochenka
We consider a problem of linear model selection in the presence of both continuous and categorical predictors. Feasible models consist of subsets of numerical variables and partiti…
Combined l_1 and greedy l_0 penalized least squares for linear model selection
Piotr Pokarowski, Jan Mielniczuk
We introduce a computationally effective algorithm for a linear model selection consisting of three steps: screening--ordering--selection (SOS). Screening of predictors is based on…
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…