An Entropy Paradox Free Fractional Diffusion Equation

A new look at the fractional diffusion equation was done. Using the unified fractional derivative, a new formulation was proposed, and the equation was solved for three different order cases: neutral, dominant time, and dominant space. The solutions were expressed by generalizations of classic formulae used for the stable distributions. The entropy paradox problem was studied and clarified through the Rényi entropy: in the extreme wave regime the entropy is −∞. In passing, Tsallis and Rényi entropies for stable distributions are introduced and exemplified.

On a Multivariate Analog of the Zolotarev Problem

A generalized multivariate problem due to V. M. Zolotarev is considered. Some related results on geometric random sums and (multivariate) geometric stable distributions are extended to a more general case of “anisotropic” random summation where sums of independent random vectors with multivariate random index having a special multivariate geometric distribution are considered. Anisotropic-geometric stable distributions are introduced. It is demonstrated that these distributions are coordinate-wise scale mixtures of elliptically contoured stable distributions with the Marshall–Olkin mixing distributions. The corresponding “anisotropic” analogs of multivariate Laplace, Linnik and Mittag–Leffler distributions are introduced. Some relations between these distributions are presented.

Random Variate Generation for Exponential and Gamma Tilted Stable Distributions

We develop a new efficient simulation scheme for sampling two families of tilted stable distributions: exponential tilted stable (ETS) and gamma tilted stable (GTS) distributions. Our scheme is based on two-dimensional single rejection. For the ETS family, its complexity is uniformly bounded over all ranges of parameters. This new algorithm outperforms all existing schemes. In particular, it is more efficient than the well-known double rejection scheme, which is the only algorithm with uniformly bounded complexity that we can find in the current literature. Beside the ETS family, our scheme is also flexible to be further extended for generating the GTS family, which cannot easily be done by extending the double rejection scheme. Our algorithms are straightforward to implement, and numerical experiments and tests are conducted to demonstrate the accuracy and efficiency.

Goodness-of-fit test for $$\alpha$$-stable distribution based on the quantile conditional variance statistics

The class of $$\alpha$$ α -stable distributions is ubiquitous in many areas including signal processing, finance, biology, physics, and condition monitoring. In particular, it allows efficient noise modeling and incorporates distributional properties such as asymmetry and heavy-tails. Despite the popularity of this modeling choice, most statistical goodness-of-fit tests designed for $$\alpha$$ α -stable distributions are based on a generic distance measurement methods. To be efficient, those methods require large sample sizes and often do not efficiently discriminate distributions when the corresponding $$\alpha$$ α -stable parameters are close to each other. In this paper, we propose a novel goodness-of-fit method based on quantile (trimmed) conditional variances that is designed to overcome these deficiencies and outperforms many benchmark testing procedures. The effectiveness of the proposed approach is illustrated using extensive simulation study with focus set on the symmetric case. For completeness, an empirical example linked to plasma physics is provided.

Representation of the density functions of a multidimensional strictly stable distributions by series of generalized functions

В статье рассматриваются многомерные строго устойчивые распределения. Как известно, функции плотности этих законов не представляются в явном виде за исключением известных законов Гаусса и Коши. Отправным пунктом для исследований являются характеристические функции. Имеется несколько различных форм их представления. В статье выбирается форма, предложенная в [1]. Применение обратного преобразования Фурье совместно с суммированием интегралов по Абелю позволило получить разложения функций плотности многомерных устойчивых распределений (см.[1], [12]). Основным результатом статьи являются представления этих функций с помощью рядов обобщенных функций над пространством Лизоркина. Они позволяют определить порядок убывания главного члена разложения на бесконечности для любого радиального направления. Кроме того, выведенные формулы дают возможность увидеть структуру формирования слагаемых в разложениях. В следствии приводятся примеры для различных случаев носителей спектральной меры многомерных устойчивых законов. The article discusses multidimensional strictly stable distributions. As is known, the density functions of these laws are not represented in closed form, with the exception of the well-known laws of Gauss and Cauchy. Characteristic functions are the starting point for research. There are several different forms of their presentation. The article chooses the form proposed in [1]. The application of the inverse Fourier transform together with the Abel summation of the integrals made it possible to obtain expansions of the density functions of multidimensional stable distributions (see [1], [12]). The main result of the article is the representation of these functions using series of generalized functions over the Lizorkin space. They make it possible to determine the order of decay of the principal term of the expansion at infinity for any radial direction. In addition, the derived formulas make it possible to see the structure of the formation of terms in expansions. In the corollary, examples are given for various cases of the support of the spectral measure of multidimensional stable laws.