Results 21 to 30 of about 233,260 (285)

Modelling Heavy Tailed Phenomena Using a LogNormal Distribution Having a Numerically Verifiable Infinite Variance

open access: yesMathematics, 2023
One-sided heavy tailed distributions have been used in many engineering applications, ranging from teletraffic modelling to financial engineering. In practice, the most interesting heavy tailed distributions are those having a finite mean and a diverging
Marco Cococcioni   +2 more
doaj   +1 more source

The Asymptotics of Moments for the Remaining Time of Heavy-Tail Distributions

open access: yesComputer Sciences & Mathematics Forum, 2023
Recent mathematical models of reliability computer systems and telecommunication networks are based on distributions with heavy tails. This paper falls into the category of exploring the classical models with heavy tails: Gnedenko–Weibull, Burr ...
Vladimir Rusev, Alexander Skorikov
doaj   +1 more source

Approximation of heavy-tailed distributions via stable-driven SDEs [PDF]

open access: yesBernoulli, 2020
Constructions of numerous approximate sampling algorithms are based on the well-known fact that certain Gibbs measures are stationary distributions of ergodic stochastic differential equations (SDEs) driven by the Brownian motion. However, for some heavy-
Lu-Jing Huang   +2 more
semanticscholar   +1 more source

QML Estimation of GARCH(1,1) Process [PDF]

open access: yesMaǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗ, 2017
In financial time series, the conventional fitting procedure (QMLE) suffers from the outlier problem. Estimation of the parameters in GARCH model, can be adversely affected by a single outlier.simulation studies will not only demonstrate the robustness ...
Mona Samy Elkhouly
doaj   +1 more source

Heavy-tailed distribution of cyber-risks [PDF]

open access: yesThe European Physical Journal B, 2010
ISSN:1434 ...
Maillart, T., Sornette, D.
openaire   +4 more sources

Generative models of simultaneously heavy-tailed distributions of interevent times on nodes and edges. [PDF]

open access: yesPhysical Review E, 2020
Intervals between discrete events representing human activities, as well as other types of events, often obey heavy-tailed distributions, and their impacts on collective dynamics on networks such as contagion processes have been intensively studied.
Elohim Fonseca dos Reis   +2 more
semanticscholar   +1 more source

Feller-Pareto and Related Distributions: Numerical Implementation and Actuarial Applications

open access: yesJournal of Statistical Software, 2022
Actuaries model insurance claim amounts using heavy tailed probability distributions. They routinely need to evaluate quantities related to these distributions such as quantiles in the far right tail, moments or limited moments.
Christophe Dutang   +2 more
doaj   +1 more source

Heavy‐tailed log hydraulic conductivity distributions imply heavy‐tailed log velocity distributions [PDF]

open access: yesWater Resources Research, 2006
Equations of contaminant transport describing non‐Gaussian dispersion of solute in heterogeneous porous media have been developed by several authors (e.g., Berkowitz and Scher (1995, 1998), Benson (1998, 2001), Berkowitz et al. (2000), and Baeumer et al. (2005)).
Matthew V. Kohlbecker   +2 more
openaire   +1 more source

The Exponential T-X Family of Distributions: Properties and an Application to Insurance Data

open access: yesJournal of Mathematics, 2021
Heavy-tailed distributions play a prominent role in actuarial and financial sciences. In this paper, we introduce a family of distributions that we refer to as exponential T-X (ETX) family.
Zubair Ahmad   +4 more
doaj   +1 more source

Heavy-Tailed Distributions and Rating [PDF]

open access: yesASTIN Bulletin, 2001
AbstractIn this paper we consider the problem raised in the Astin Bulletin (1999) by Prof. Benktander at the occasion of his 80th birthday concerning the choice of an appropriate claim size distribution in connection with reinsurance rating problems. Appropriate models for large claim distributions play a central role in this matter.
J. Beirlant, G. Matthys, G. Dierckx
openaire   +1 more source

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