Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Kołodziejek, Bartosz"'
The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the $\mathbf{gips}$ package in R, which identifies permutation subgroup symmetries in Gaussian vectors. $\mathbf{gips}$ serves two main
Externí odkaz:
http://arxiv.org/abs/2307.00790
Bayesian statistical graphical models are typically either continuous and parametric (Gaussian, parameterized by the graph-dependent precision matrix with Wishart-type priors) or discrete and non-parametric (with graph-dependent structure of probabil
Externí odkaz:
http://arxiv.org/abs/2301.06058
We study properties of two resampling scenarios: Conditional Randomisation and Conditional Permutation schemes, which are relevant for testing conditional independence of discrete random variables $X$ and $Y$ given a random variable $Z$. Namely, we i
Externí odkaz:
http://arxiv.org/abs/2210.01516
We consider multivariate centered Gaussian models for the random vector $(Z^1,\ldots, Z^p)$, whose conditional structure is described by a homogeneous graph and which is invariant under the action of a permutation subgroup. The following paper concer
Externí odkaz:
http://arxiv.org/abs/2207.13330
Autor:
Bogdan, Małgorzata, Dupuis, Xavier, Graczyk, Piotr, Kołodziejek, Bartosz, Skalski, Tomasz, Tardivel, Patrick, Wilczyński, Maciej
SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be equal in absolute value (clustering). Consequently, SLOPE may eliminate
Externí odkaz:
http://arxiv.org/abs/2203.12086
Sorted $\ell_1$ Penalized Estimator (SLOPE) is a relatively new convex regularization method for fitting high-dimensional regression models. SLOPE allows to reduce the model dimension by shrinking some estimates of the regression coefficients complet
Externí odkaz:
http://arxiv.org/abs/2202.08573
Publikováno v:
Advances in Mathematics, 403 (2022), 108398: 1 - 50
We study the behavior of the tail of a measure $\mu^{\boxtimes t}$, where $\boxtimes t$ is the $t$-fold free multiplicative convolution power for $t\geq 1$. We focus on the case where $\mu$ is a probability measure on the positive half-line with a re
Externí odkaz:
http://arxiv.org/abs/2105.07836
We consider multivariate centered Gaussian models for the random variable $Z=(Z_1,\ldots, Z_p)$, invariant under the action of a subgroup of the group of permutations on $\{1,\ldots, p\}$. Using the representation theory of the symmetric group on the
Externí odkaz:
http://arxiv.org/abs/2004.03503
We study solutions to the stochastic fixed point equation $X\stackrel{d}{=}AX+B$ where the coefficients $A$ and $B$ are nonnegative random variables. We introduce the ``local dependence measure'' (LDM) and its Legendre-type transform to analyze the l
Externí odkaz:
http://arxiv.org/abs/2004.01850
Autor:
Damek, Ewa, Kołodziejek, Bartosz
Publikováno v:
Electron. Commun. Probab. 23 (2018), str. 1 - 13
We study tails of the supremum of a perturbed random walk under regime which was not yet considered in the literature. Our approach is based on a new renewal theorem, which is of independent interest. We obtain first and second order asymptotics of t
Externí odkaz:
http://arxiv.org/abs/1812.04496