Zobrazeno 1 - 10
of 878
pro vyhledávání: '"Sirota L"'
We establish an ordinary as well as a logarithmical convexity of the Moment Generating Function (MGF) for the centered random variable and vector (r.v.) satisfying the Kramer's condition. Our considerations are based on the theory of the so-called Gr
Externí odkaz:
http://arxiv.org/abs/2409.05085
We derive in this short report the exact exponential decreasing tail of distribution for naturel normed sums of independent centered random variables (r.v.), applying the theory of Grand Lebesgue Spaces (GLS). We consider also some applications into
Externí odkaz:
http://arxiv.org/abs/2409.05083
We offer in this short report the so-called adaptive functional smoothness estimation in the Hilbert space norm sense in the three classical problems of non-parametrical statistic: regression, density and spectral (density) function measurement (esti
Externí odkaz:
http://arxiv.org/abs/2409.00491
We study moment rearrangement invariant spaces, which contain as particular cases the generalized Grand Lebesgue Spaces, and provide norm estimates for some operators, not necessarily linear, acting between some measurable rearrangement invariant spa
Externí odkaz:
http://arxiv.org/abs/2212.08937
Autor:
Ostrovsky, E., Sirota, L.
We represent in this preprint the exact estimate for covariation berween two random variables (r.v.), which are measurable relative the corresponding sigma-algebras through anyhow mixing coefficients. We associate a solution of this problem with fund
Externí odkaz:
http://arxiv.org/abs/2206.02822
We find the exponential exact two-terms non-asymptotic expression for the maximum and minimum distribution of a non-Gaussian, in general case, random vector.
Externí odkaz:
http://arxiv.org/abs/2206.02773
We establish the one-to one bilateral interrelations between an asymptotic behavior for the tail of distributions for random variables and its great moments evaluation. Our results generalize the famous Richter's ones.
Externí odkaz:
http://arxiv.org/abs/2206.00624
We derive in this short report the exponential as well as power decreasing tail estimations for the sums of centered exchangeable random variables, alike ones for the sums of the centered independent ones.
Externí odkaz:
http://arxiv.org/abs/2206.00620
We deduce an extension theorem for the so-called Sobolev-Grand Lebesgue Spaces defined on the suitable subsets of the whole finite-dimensional Euclidean space, and estimate the norms of correspondent extension operator, which may be choosed as linear
Externí odkaz:
http://arxiv.org/abs/2206.00617
We will prove that by averaging of random variables (r.v.) and random fields (r.f.) its tails of distributions do not increase in comparison with the tails of source variables, essentially or almost exact, under very weak conditions.
Externí odkaz:
http://arxiv.org/abs/2206.00537