Results 31 to 40 of about 2,212,997 (349)
A Maximum Entropy Approach to Loss Distribution Analysis
Entropy, 2013 In this paper we propose an approach to the estimation and simulation of loss distributions based on Maximum Entropy (ME), a non-parametric technique that maximizes the Shannon entropy of the data under moment constraints. Special cases of the ME density
Marco Bee
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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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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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Heavy-tailed fractional Pearson diffusions [PDF]
Stochastic Processes and their Applications, 2017 We define heavy-tailed fractional reciprocal gamma and Fisher-Snedecor diffusions by a non-Markovian time change in the corresponding Pearson diffusions. Pearson diffusions are governed by the backward Kolmogorov equations with space-varying polynomial coefficients and are widely used in applications.
Leonenko, Nikolai +3 more
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Minimum of heavy-tailed random variables is not heavy tailed
AIMS Mathematics, 2023 <abstract><p>By constructing an appropriate example, we show that the class of heavy-tailed distributions is not closed under minimum. We provide two independent heavy-tailed random variables, such that their minimum is not heavy tailed. In addition, we establish a few properties of the distributions considered in the example.</p></
Leipus, Remigijus +2 more
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What controls the tail behaviour of flood series: rainfall or runoff generation? [PDF]
Hydrology and Earth System SciencesMany observed time series of precipitation and streamflow show heavy-tail behaviour. For heavy-tailed distributions, the occurrence of extreme events has a higher probability than for distributions with an exponentially receding tail. If we neglect heavy-
E. Macdonald +7 more
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Linear Regression for Heavy Tails [PDF]
Risks, 2018 There exist several estimators of the regression line in the simple linear regression: Least Squares, Least Absolute Deviation, Right Median, Theil–Sen, Weighted Balance, and Least Trimmed Squares. Their performance for heavy tails is compared below on the basis of a quadratic loss function.
Balkema, Guus, Embrechts, Paul
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Epilepsy Seizure Detection: A Heavy Tail Approach
IEEE Access, 2020 Epilepsy is a chronic brain disorder that affects the quality of life of many patients even when this disease is being controlled. If we want to improve those lives affected, we need to perform real-time epilepsy detection with constant monitoring of the
Jesus G. Servin-Aguilar +4 more
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Optimal heavy tail estimation – Part 1: Order selection [PDF]
Nonlinear Processes in Geophysics, 2017 The tail probability, P, of the distribution of a variable is important for risk analysis of extremes. Many variables in complex geophysical systems show heavy tails, where P decreases with the value, x, of a variable as a power law with a ...
M. Mudelsee, M. Mudelsee, M. A. Bermejo
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Estimating Heavy-Tail Exponents Through Max Self–Similarity [PDF]
IEEE Transactions on Information Theory, 2006 In this paper, a novel approach to the problem of estimating the heavy-tail exponent α >; 0 of a distribution is proposed. It is based on the fact that block-maxima of size m scale at a rate m1/α for independent, as well as for a number of dependent data.
Stilian A. Stoev +2 more
semanticscholar +1 more source