Publication year: 2020
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This textbook highlights the many practical uses of stable distributions, exploring the theory, numerical algorithms, and statistical methods used to work with stable laws. Because of the author’s accessible and comprehensive approach, readers will be able to understand and use these methods. Both mathematicians and non-mathematicians will find this a valuable resource for more accurately modelling and predicting large values in a number of real-world scenarios.The following chapters present the theory of stable distributions, a wide range of applications, and statistical methods, with the final chapters focusing on regression, signal processing, and related distributions. Each chapter ends with a number of carefully chosen exercises. Links to free software are included as well, where readers can put these methods into practice.
Subject: Mathematics and Statistics, Probability Theory and Stochastic Processes, Applications of Mathematics, Statistical Theory and Methods, Stable distributions, Univariate stable distributions, Heavy tailed data, Heavy tailed data distributions, Generalized central limit theorem, Signal processing algorithm, Univariate estimate, Stable regression, Pareto distributions, Multivariate stable dsitribution, Stable laws, Stable random variables, Brownian motion, Extreme value estimation, Stable filter