Results 21 to 30 of about 85,681 (262)
Randomly Stopped Sums with Generalized Subexponential Distribution
Axioms, 2023 Let {ξ1,ξ2,…} be a sequence of independent possibly differently distributed random variables, defined on a probability space (Ω,F,P) with distribution functions {Fξ1,Fξ2,…}. Let η be a counting random variable independent of sequence {ξ1,ξ2,…}.
Jūratė Karasevičienė, Jonas Šiaulys
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Inferring heavy tails of flood distributions through hydrograph recession analysis [PDF]
Hydrology and Earth System Sciences, 2023 Floods are often disastrous due to underestimation of the magnitude of rare events. Underestimation commonly happens when the magnitudes of floods follow a heavy-tailed distribution, but this behavior is not recognized and thus neglected for flood hazard
H.-J. Wang +6 more
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Outliers and the Ostensibly Heavy Tails [PDF]
Mathematical Methods of Statistics, 2019 The aim of the paper is to show that the presence of one possible type of outliers is not connected to that of heavy tails of the distribution. In contrary, typical situation for outliers appearance is the case of compact supported distributions.
Klebanov, L., Volchenkova, I.
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Fractal and Fractional, 2023 In this paper, a fractional Weibull process is utilized in a predictive stochastic differential equation model to allow for skewness and heavy-tailed characteristics.
Wanqing Song, Dongdong Chen, Enrico Zio
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Indagationes Mathematicae, 2023 The present paper is concerned with the stationary workload of queues with heavy-tailed (regularly varying) characteristics. We adopt a transform perspective to illuminate a close connection between the tail asymptotics and heavy-traffic limit in infinite-variance scenarios. This serves as a tribute to some of the pioneering results of J.W.
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MODEL OF THE DIFFUSION PROCESS WITH HEAVY TAILS IN THE DISTRIBUTION [PDF]
Vestnik KRAUNC: Fiziko-Matematičeskie Nauki, 2017 A generator of stable random variables is developed.
Zakharov I. S.
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IEEE Transactions on Information Theory, 2013 The stochastic multi-armed bandit problem is well understood when the reward distributions are sub-Gaussian. In this paper we examine the bandit problem under the weaker assumption that the distributions have moments of order 1+ε, for some $ε\in (0,1]$.
S. Bubeck, N. Cesa-Bianchi, G. Lugosi
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Minimax Policy for Heavy-tailed Bandits [PDF]
2021 American Control Conference (ACC), 2021 We study the stochastic Multi-Armed Bandit (MAB) problem under worst-case regret and heavy-tailed reward distribution. We modify the minimax policy MOSS for the sub-Gaussian reward distribution by using saturated empirical mean to design a new algorithm called Robust MOSS.
Lai Wei 0002, Vaibhav Srivastava
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Journal of the American Statistical Association, 2014 An unobserved components model in which the signal is buried in noise that is non-Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation driven model, based on a conditional Student t-distribution, that is tractable and retains some of the desirable features of the linear Gaussian model.
Andrew Harvey, LUATI, ALESSANDRA
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Adaptive Models and Heavy Tails [PDF]
SSRN Electronic Journal, 2016 This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms.
Davide Delle Monache, Ivan Petrella
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