13 citations · 18 across the 6 of their papers we have counts for
7 papers
Enhancing naive classifier for positive unlabeled data based on logistic regression approach
Mateusz Płatek, Jan Mielniczuk
We argue that for analysis of Positive Unlabeled (PU) data under Selected Completely At Random (SCAR) assumption it is fruitful to view the problem as fitting of misspecified model…
Analysis of Conditional Randomisation and Permutation schemes with application to conditional independence testing
Małgorzata Łazęcka, Bartosz Kołodziejek, Jan Mielniczuk
We study properties of two resampling scenarios: Conditional Randomisation and Conditional Permutation schemes, which are relevant for testing conditional independence of discrete…
Double logistic regression approach to biased positive-unlabeled data
Konrad Furmańczyk, Jan Mielniczuk, Wojciech Rejchel +1
Positive and unlabelled learning is an important problem which arises naturally in many applications. The significant limitation of almost all existing methods lies in assuming tha…
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…
Selection consistency of Lasso-based procedures for misspecified high-dimensional binary model and random regressors
Mariusz Kubkowski, Jan Mielniczuk
We consider selection of random predictors for high-dimensional regression problem with binary response for a general loss function. Important special case is when the binary model…
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…