We show that, although nonlinear optics may give rise to a vast multitude of statistics, all these statistics converge, in their extreme-value limit, to one of a few universal extreme-value statistics. Specifically, in the class of polynomial nonlinearities, such as those found in the Kerr effect, weak-field harmonic generation, and multiphoton ionization, the statistics of the nonlinear-optical output converges, in the extreme-value limit, to the exponentially tailed, Gumbel distribution. Exponentially growing nonlinear signals, on the other hand, such as those induced by parametric instabilities and stimulated scattering, are shown to reach their extreme-value limits in the class of the Fréchet statistics, giving rise to extreme-value distributions (EVDs) with heavy, manifestly nonexponential tails, thus favoring extreme-event outcomes and rogue-wave buildup.
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Sci Rep
January 2025
Laboratory of Veterinary Epidemiology, Graduate Program in Veterinary Medicine, Universidade Federal de Pelotas, University Campus, Building 42, Post Office Box 354, Capão do Leão, RS, CEP 96010-900, Brazil.
Dengue remains a significant public health concern in Brazil, with all federative units registering occurrences of the disease within their territories despite constant measures to control the Aedes aegypti vector. This study aimed to evaluate the profile of notified dengue cases in the Brazilian Legal Amazon from 2001 to 2021, analyzing National System of Notifiable Diseases (SINAN) data on the disease to assess the risks for its occurrence. Subsequently, statistical analyses were conducted to identify incidence and lethality rates.
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January 2025
Department of Statistics, Federal Govt. Quaid-i-Azam Degree College, Hamd Ullah, National University of Pakistan, Rawalpindi, Pakistan.
The study has investigated the implications of three estimation methods, namely L-moments, Maximum Likelihood, and Maximum Product of Spacing (MPS), for fitting the four-parameter Kappa Distribution (KAPD) in extreme value analysis using Monte Carlo simulations. The accuracy of the estimates has been evaluated using root mean square error (RMSE) and bias. The paper also includes an analysis of the effect of the estimation method on the estimated quantiles considering a real-life example of annual maximum peak flows and the Generalized Normal Distribution as the error distribution.
View Article and Find Full Text PDFHeliyon
April 2024
Department of Mathematics, College of Science, University of Bisha, P.O. Box 551, Bisha 61922, Saudi Arabia.
The premise of extreme value theory focuses on the stochastic behaviour and occurrence of extreme observations in an event that is random. Traditionally for univariate case, the behaviour of the maxima is described either by the types-I, types-II or types-III extreme value distributions, primarily known as the Gumbel, Fréchet or reversed Weibull models. These are all particular cases of the generalized extreme value ( ) model.
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December 2024
Forest Glen Consulting, Brighton, UT, USA.
Extreme value theory provides a direct means to characterize the distribution of high emitters within a vehicle fleet and calculate statistical confidence intervals for comparisons. Defining a "high emitter" as the maximum emitter in a random sample of N vehicles implies in the limit of large N that high emitters follow an extreme value distribution, comprised of three distinct domains. The analysis of over twenty years of roadside remote sensing emissions measurements in Chicago, Denver, Los Angeles and Tulsa reveals clear differences between gasoline vehicle high emitter distributions across pollutants (hydrocarbons (HC), carbon monoxide (CO) and nitric oxide (NO)), but very similar behavior across the four cities.
View Article and Find Full Text PDFBiophys Rev
October 2024
The Institute for Solid State Physics, The University of Tokyo, Kashiwano-Ha 5-1-5, Kashiwa, Chiba 277-8581 Japan.
Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fréchet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems.
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