5 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…
Structure learning for CTBN's via penalized maximum likelihood methods
Maryia Shpak, Błażej Miasojedow, Wojciech Rejchel
The continuous-time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions…
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…
Rank-based Lasso -- efficient methods for high-dimensional robust model selection
Wojciech Rejchel, Malgorzata Bogdan
We consider the problem of identifying significant predictors in large data bases, where the response variable depends on the linear combination of explanatory variables through an…
Asymptotics of maximum likelihood estimators based on Markov chain Monte Carlo methods
Błażej Miasojedow, Wojciech Niemiro, Wojciech Rejchel
In many complex statistical models maximum likelihood estimators cannot be calculated. In the paper we solve this problem using Markov chain Monte Carlo approximation of the true l…