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Heavy-tailed distributions : data, diagnostics, and new developments [PDF]
, 2011 This monograph is written for the numerate nonspecialist, and hopes to serve three purposes. First it gathers mathematical material from diverse but related fields of order statistics, records, extreme value theory, majorization, regular variation and ...
Nieboer, Daan, Cooke, Roger M
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QML Estimation of GARCH(1,1) Process [PDF]
Maǧallaẗ Al-Buḥūṯ Al-Mālīyyaẗ wa Al-Tiğāriyyaẗ, 2017 In financial time series, the conventional fitting procedure (QMLE) suffers from the outlier problem. Estimation of the parameters in GARCH model, can be adversely affected by a single outlier.simulation studies will not only demonstrate the robustness ...
Mona Samy Elkhouly
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Feller-Pareto and Related Distributions: Numerical Implementation and Actuarial Applications
Journal of Statistical Software, 2022 Actuaries model insurance claim amounts using heavy tailed probability distributions. They routinely need to evaluate quantities related to these distributions such as quantiles in the far right tail, moments or limited moments.
Christophe Dutang +2 more
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The Exponential T-X Family of Distributions: Properties and an Application to Insurance Data
Journal of Mathematics, 2021 Heavy-tailed distributions play a prominent role in actuarial and financial sciences. In this paper, we introduce a family of distributions that we refer to as exponential T-X (ETX) family.
Zubair Ahmad +4 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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Estimation for a non-stationary semi-strong GARCH(1,1) model with heavy-tailed errors [PDF]
, 2010 This paper studies the estimation of a semi-strong GARCH(1,1) model when it does not have a stationary solution, where semi-strong means that we do not require the errors to be independent over time.
Linton, O., Wang, H., Pan, J.
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Particle filtering with alpha-stable distributions [PDF]
, 2005 In this paper we introduce a novel sequential Monte Carlo technique, which is based on the family of symmetric alpha- stable (SAS) distributions.
Canagarajah, N. +4 more
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On Learning Mixtures of Heavy-Tailed Distributions [PDF]
46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05), 2005 We consider the problem of learning mixtures of arbitrary symmetric distributions. We formulate sufficient separation conditions and present a learning algorithm with provable guarantees for mixtures of distributions that satisfy these separation conditions. Our bounds are independent of the variances of the distributions; to the best of our knowledge,
Anirban Dasgupta 0001 +3 more
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Understanding the structure of cognitive noise.
PLoS Computational Biology, 2022 Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure.
Jian-Qiao Zhu +3 more
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Fitting Heavy Tailed Distributions: The poweRlaw Package
Journal of Statistical Software, 2015 Over the last few years, the power law distribution has been used as the data generating mechanism in many disparate fields. However, at times the techniques used to fit the power law distribution have been inappropriate.
Colin S. Gillespie
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