Results 221 to 230 of about 41,863 (267)
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Inverse Laplace transform for heavy-tailed distributions
Applied Mathematics and Computation, 2004Here the Laplace transform inversion on the real line of heavy-tailed (probability) density functions is considered. The method assumes as known a finite set of fractional moments drawn from real values of the Laplace transform by fractional calculus.
Tagliani, Aldo, Y. VELAZQUEZ
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2021
In Sec. 2.3.3 we introduced the idea of heavy-tailed distributions from a purely mathematical point of view, where we considered probability distributions for which the central limit theorem does not apply. The terminology arises because the “tails” of the distribution, that is the parts where the variable |x| → ∞, decrease so slowly that in most cases
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In Sec. 2.3.3 we introduced the idea of heavy-tailed distributions from a purely mathematical point of view, where we considered probability distributions for which the central limit theorem does not apply. The terminology arises because the “tails” of the distribution, that is the parts where the variable |x| → ∞, decrease so slowly that in most cases
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Inference for heavy tailed distributions
Journal of Statistical Planning and Inference, 1998This work develops the statistical inference for stable laws of order \(\alpha\) and an asymmetry parameter \(\beta\), based on an independent sample of size \(n\geq 1\). Three different approaches to the construction of confidence intervals for the mean \(\mu\) are proposed, two of them involving bootstrap.
Athreya, K. B., Lahiri, S. N., Wu, Wei
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Approximating expected shortfall for heavy-tailed distributions
Econometrics and Statistics, 2018A saddlepoint approximation for evaluating the expected shortfall of financial returns under realistic distributional assumptions is derived. This addresses a need that has arisen after the Basel Committee’s proposed move from Value at Risk to expected shortfall as the mandated risk measure in its market risk framework.
Broda, Simon A +2 more
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Box–Cox transformations and heavy-tailed distributions
Journal of Applied Probability, 2004It is a stylized fact that estimators in extreme-value theory suffer from serious bias. Moreover, graphical representations of extremal data often show erratic behaviour. In the statistical literature it is advised to use a Box–Cox transformation in order to make data more suitable for statistical analysis. We provide some of the theoretical background
Teugels, Jef L., Vanroelen, Giovanni
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Distributions with Heavy Tails in Orlicz Spaces
Journal of Theoretical Probability, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Konstantinides, Dimitrios G. +1 more
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Structural Equation Modeling with Heavy Tailed Distributions
Psychometrika, 2004Data in social and behavioral sciences typically possess heavy tails. Structural equation modeling is commonly used in analyzing interrelations among variables of such data. Classical methods for structural equation modeling fit a proposed model to the sample covariance matrix, which can lead to very inefficient parameter estimates.
Yuan, Ke-Hai +2 more
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Performance Analysis with Truncated Heavy-Tailed Distributions
Methodology and Computing in Applied Probability, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Asmussen, S., Pihlsgård, M.
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Estimating the Mean of Heavy-Tailed Distributions
Extremes, 2003For i.i.d. observations \(X=(X_1,\dots,X_n)\) with CDF \[ F(x)=1-cx^{-1/\xi}(1+x^{-\delta}L(x)) \] (\(L\) being a slowly varying function) the problem of mean \({\mathbf E}X_1\) estimation is considered in the case \(\xi\in(1/2,1)\). (For \(\xi\in (0,1/2)\) the sample mean is an asymptotically normal estimate of \({\mathbf E}X_1\), for \(\xi>1\) the ...
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Latest developments on heavy-tailed distributions
Journal of Econometrics, 2013The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. The recent financial and economic crises have shown the dangers of assuming that the risks are nearly Gaussian distributed. In particular, non-causal representations are not identified in the case of Gaussian AR processes.
Paolella, Marc +3 more
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