Results 21 to 30 of about 26,472 (303)
Heavy or semi-heavy tail, that is the question
Journal of Applied Statistics, 2020 While there has been considerable research on the analysis of extreme values and outliers by using heavy-tailed distributions, little is known about the semi-heavy-tailed behaviors of data when there are a few suspicious outliers. To address the situation where data are skewed possessing semi-heavy tails, we introduce two new skewed distribution ...
Jamil, Ownuk +2 more
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Data in Brief, 2020 In this data article, we investigated the accumulation of heavy metals in the lizard Microlophus atacamensis, in three coastal areas of the Atacama Desert, northern Chile. We captured lizards in a non-intervened area (Parque Nacional Pan de Azucar, PAZ),
Yery Marambio-Alfaro +8 more
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Estimation of heavy tails in optical non-linear processes
New Journal of Physics, 2021 In optical non-linear processes, rogue waves can be observed, which can be mathematically described by heavy-tailed distributions. These distributions are special since the probability of registering extremely high intensities is significantly higher ...
Éva Rácz +2 more
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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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WIREs Computational Statistics, 2012 AbstractThe concept of heavy‐ or long‐tailed densities (or distributions) has attracted much well‐deserved attention in the literature. A quick search in Google using the keywords long‐tailed statistics retrieves almost 12 million items. The concept has become a pillar of the theory of extremes, and through its connection with outlier‐prone ...
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CoRR, 2023 In this paper, we study multi-armed bandits (MAB) and stochastic linear bandits (SLB) with heavy-tailed rewards and quantum reward oracle. Unlike the previous work on quantum bandits that assumes bounded/sub-Gaussian distributions for rewards, here we investigate the quantum bandits problem under a weaker assumption that the distributions of rewards ...
Yulian Wu +3 more
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A Note on Second Order Conditions in Extreme Value Theory: Linking General and Heavy Tail Conditions
Revstat Statistical Journal, 2007 Second order conditions ruling the rate of convergence in any first order condition involving regular variation and assuring a unified extreme value limiting distribution function for the sequence of maximum values, linearly normalized, have appeared in
M. Isabel Fraga Alves +3 more
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