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We obtain an uniform tail estimates for natural normed sums of independent random variables (r.v.) with regular varying tails of distributions. We give also many examples on order to show the exactness of offered estimates and discuss some…

Probability · Mathematics 2012-06-22 E. Ostrovsky , L. Sirota

We present in this paper the theory of multivariate Banach spaces of random variables with exponential decreasing tails of distributions.

Probability · Mathematics 2015-10-15 E. Ostrovsky , L. Sirota

We introduce a Banach rearrangement invariant (tail) quasy-norm by means of Hardy's (Cesaro) average on the (measurable) functions defined on some measurable space which is a slight generalization of classical Lorentz-Marcinkiewicz norm and…

Functional Analysis · Mathematics 2012-11-28 E. Ostrovsky , L. Sirota

We investigate a way of comparing and classifying tails of random variables. Our approach extends the notion of classical indices, such as exponential and moment indices, which are widely used measuring heaviness of tail functions. A…

Probability · Mathematics 2013-10-07 Jaakko Lehtomaa

We study probability inequalities leading to tail estimates in a general semigroup $\mathscr{G}$ with a translation-invariant metric $d_{\mathscr{G}}$. (An important and central example of this in the functional analysis literature is that…

Probability · Mathematics 2020-07-27 Apoorva Khare , Bala Rajaratnam

When a spatial process is recorded over time and the observation at a given time instant is viewed as a point in a function space, the result is a time series taking values in a Banach space. To study the spatio-temporal extremal dynamics…

Probability · Mathematics 2010-01-20 Thomas Meinguet , Johan Segers

For a fixed positive integer $\;k,\;$ limit laws of linearly normalized $\;k$-th upper order statistics are well known. In this article, a comprehensive study of tail behaviours of limit laws of normalized $k$-th upper order statistics…

Probability · Mathematics 2015-12-11 Sreenivasan Ravi , Mandagere Chandrashekhar Manohar

We derive the tail inequalities between two random variables starting from inequalities between its moment, or more generally between its Lebesgue-Riesz norms, which holds true on certain sets of parameters. We consider some applications…

Probability · Mathematics 2022-06-06 M. R. Formica , E. Ostrovsky , L. Sirota

We obtain sufficient conditions for belonging of almost all paths of a random process to some fixed rearrangement invariant (r.i.) Banach functional space, and to satisfying the Central Limit Theorem (CLT) in this space. We describe also…

Probability · Mathematics 2014-09-09 E. Ostrovsky , L. Sirota

We derive in this article the asymptotic behavior as well as non-asymptotical estimates of tail of distribution for self-normalized sums of random variables (r.v.) under natural classical norming. We investigate also the case of…

Probability · Mathematics 2017-10-10 E. Ostrovsky , L. Sirota

We derive in this preprint the moment and exponential tail estimates, sufficient conditions for the Non-Central Limit Theorem (NCLT) in the ordinary one-dimensional space as well as in the space of continuous functions for the properly…

Probability · Mathematics 2017-10-17 E. Ostrovsky , L. Sirota

We find the exact values for constants in bilateral Calderon-Stein-Weiss inequalities between tail (Marcinkiewicz) norm and weak Lebesgue (Lorentz) norm. Possible applications: Functional Analysis (for instance, interpolation of operators),…

Functional Analysis · Mathematics 2012-10-18 E. Ostrovsky , L. Sirota

We derive the exponential as well as power decreasing tail estimations for normed sums of centered independent identical distributed (or not) random variables on the Khintchine's form. We consider arbitrary, in particular, non-Rademacher's…

Probability · Mathematics 2021-10-06 M. R. Formica , E. Ostrovsky , L. Sirota

We study the random variables (r.v.) with values in the so-called mixed (anisotropic) Lebesgue-Riesz spaces: formulate the sufficient conditions for belonging of the r.v. to these spaces, estimate the tail of norms distribution, especially…

Probability · Mathematics 2021-10-08 M. R. Formica , E. Ostrovsky , L. Sirota

We provide a new extension of Breiman's Theorem on computing tail probabilities of a product of random variables to a multivariate setting. In particular, we give a complete characterization of regular variation on cones in $[0,\infty)^d$…

Probability · Mathematics 2020-06-09 Bikramjit Das , Vicky Fasen-Hartmann , Claudia Klüppelberg

This paper explores some important aspects of the theory of rearrangement-invariant quasi-Banach function spaces. We focus on two main topics. Firstly, we prove an analogue of the Luxemburg representation theorem for rearrangement-invariant…

Functional Analysis · Mathematics 2025-10-15 Anna Musilová , Aleš Nekvinda , Dalimil Peša , Hana Turčinová

The existence of a Banach limit as a translation invariant positive continuous linear functional on the space of bounded scalar sequences which is equal to 1 at the constant sequence (1,1,...,1,...) is proved in a first course on functional…

Functional Analysis · Mathematics 2019-06-12 M. A. Sofi

Using an intrinsic approach, we study some properties of random fields which appear as tail fields of regularly varying stationary random fields. The index set is allowed to be a general locally compact Hausdorff Abelian group $\mathbb{G}$.…

Probability · Mathematics 2023-01-11 Günter Last

We study the tail behaviour of measurable functions under generalized Riesz-type operators in the framework of Grand Lebesgue Spaces. By exploiting the connection between the growth of $L^p$ norms and the Young--Fenchel transform, we derive…

Functional Analysis · Mathematics 2026-04-01 Maria Rosaria Formica , Eugene Ostrovsky , Leonid Sirota

The paper has two main goals. The first is to take a new approach to rearrangements on certain classes of measurable real-valued functions on $\mathbb{R}^n$. Rearrangements are maps that are monotonic (up to sets of measure zero) and…

Metric Geometry · Mathematics 2022-02-15 Gabriele Bianchi , Richard J. Gardner , Paolo Gronchi , Markus Kiderlen
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