Add to ORKGWe determine bounds of the tail probability for a sum of n independent ran-dom variables. Our assumption on these variables is non-standard: we suppose that they have moments of order δ with δ ∈ (1, 2). Some numerical examples illustrate the theoretical results.
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
8 pages, 2 figures.We determine bounds of the tail probability for a sum of $n$ independent random v...
8 pages, 2 figures.We determine bounds of the tail probability for a sum of $n$ independent random v...
Known Bernstein-type upper bounds on the tail probabilities for sums of independent zero-mean sub-ex...
We establish a new concentration-of-measure inequality for the sum of independent random variables w...
We prove a bound of the tail probability for a sum of n independent random variables. It can be appl...
Short noteIn this note we prove a bound of the tail probability for a sum of $n$ independent random ...
Short noteIn this note we prove a bound of the tail probability for a sum of $n$ independent random ...
Abstract. Let X1,..., Xn be i.i.d. integral valued random variables and Sn their sum. In the case wh...
In this contribution, the upper bounds for sums of dependent ran-dom variables Xl + X 2 +... + Xn de...
Contains fulltext : 181118.pdf (publisher's version ) (Open Access
DoctoralIn this note we introduce and discuss a few concentration tools for the study of concentrati...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
8 pages, 2 figures.We determine bounds of the tail probability for a sum of $n$ independent random v...
8 pages, 2 figures.We determine bounds of the tail probability for a sum of $n$ independent random v...
Known Bernstein-type upper bounds on the tail probabilities for sums of independent zero-mean sub-ex...
We establish a new concentration-of-measure inequality for the sum of independent random variables w...
We prove a bound of the tail probability for a sum of n independent random variables. It can be appl...
Short noteIn this note we prove a bound of the tail probability for a sum of $n$ independent random ...
Short noteIn this note we prove a bound of the tail probability for a sum of $n$ independent random ...
Abstract. Let X1,..., Xn be i.i.d. integral valued random variables and Sn their sum. In the case wh...
In this contribution, the upper bounds for sums of dependent ran-dom variables Xl + X 2 +... + Xn de...
Contains fulltext : 181118.pdf (publisher's version ) (Open Access
DoctoralIn this note we introduce and discuss a few concentration tools for the study of concentrati...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...
If a random variable is not exponentially integrable, it is known that no concentration inequality h...