Results 31 to 40 of about 1,729,239 (286)
Transformation kernel density estimation of actuarial loss functions [PDF]
, 2013 [cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a
Guillén, Montserrat +2 more
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How extreme is extreme? An assessment of daily rainfall distribution tails [PDF]
Hydrology and Earth System Sciences, 2013 The upper part of a probability distribution, usually known as the tail, governs both the magnitude and the frequency of extreme events. The tail behaviour of all probability distributions may be, loosely speaking, categorized into two families: heavy ...
S. M. Papalexiou +2 more
doaj +1 more source
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
doaj +1 more source
Algorithms, 2012 We present evidence that one can calculate generically combinatorially expensive Lp and lp averages, 0 < p < 1, in polynomial time by restricting the data to come from a wide class of statistical distributions.
John E. Lavery
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Heavy-Tailed Distributions and Rating [PDF]
ASTIN 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
The Random Effect Transformation for Three Regularity Classes
MathematicsWe continue the analysis of the influence of the random effect transformation on the regularity of distribution functions. The paper considers three regularity classes: heavy-tailed distributions, distributions with consistently varying tails, and ...
Jonas Šiaulys +2 more
doaj +1 more source
On the Accuracy of Inference on Heavy-Tailed Distributions [PDF]
Theory of Probability & Its Applications, 2014 This paper suggests a simple method of deriving nonparametric lower bounds of the accuracy of statistical inference on heavy-tailed distributions. We present lower bounds of the mean squared error of the tail index, the tail constant, and extreme quantiles estimators. The results show that the normalizing sequences of robust estimators must depend in a
openaire +1 more source
Heavy-Tailed Distributions in VaR Calculations [PDF]
, 2011 Market risks are the prospect of financial losses – or gains – due to unexpected changes in market prices and rates. Evaluating the exposure to such risks is nowadays of primary concern to risk managers in financial and non-financial institutions alike. Since the early 1990s a commonly used market risk estimation methodology has been the Value at Risk (
Adam Misiorek, Rafal Weron
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Type-I heavy tailed family with applications in medicine, engineering and insurance.
PLoS ONE, 2020 In the present study, a new class of heavy tailed distributions using the T-X family approach is introduced. The proposed family is called type-I heavy tailed family. A special model of the proposed class, named Type-I Heavy Tailed Weibull (TI-HTW) model
Wei Zhao +4 more
doaj +1 more source
$\ell_1$-regression with Heavy-tailed Distributions
CoRR, 2018 In this paper, we consider the problem of linear regression with heavy-tailed distributions. Different from previous studies that use the squared loss to measure the performance, we choose the absolute loss, which is capable of estimating the conditional median. To address the challenge that both the input and output could be heavy-tailed, we propose a
Lijun Zhang 0005, Zhi-Hua Zhou
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