Stable random vectors in Hilbert space

1989 ◽  
pp. 172-182
Author(s):  
A. D. Lisitsky
Keyword(s):  
Hilbert Space ◽  
Random Vectors ◽  
Stable Random Vectors

On dispersion of stable random vectors and its application in the prediction of multivariate stable processes

1994 ◽  
Vol 31 (3) ◽  
pp. 691-699 ◽  
Author(s):  
A. Reza Soltani ◽  
R. Moeanaddin
Keyword(s):  
Stable Process ◽  
Stable Processes ◽  
Random Vectors ◽  
Linear Predictor ◽  
Error Vector ◽  
Best Linear Predictor ◽  
Definition Of ◽  
Stable Random Vectors

Our aim in this article is to derive an expression for the best linear predictor of a multivariate symmetric α stable process based on many past values. For this purpose we introduce a definition of dispersion for symmetric α stable random vectors and choose the linear predictor which minimizes the dispersion of the error vector.

scholarly journals Limiting Behavior of the Partial Sum for Negatively Superadditive Dependent Random Vectors in Hilbert Space

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Mi-Hwa Ko
Keyword(s):  
Hilbert Space ◽  
Complete Convergence ◽  
Random Vectors ◽  
Limiting Behavior ◽  
Partial Sum

In this paper, We study the complete convergence and Lp- convergence for the maximum of the partial sum of negatively superadditive dependent random vectors in Hilbert space. The results extend the corresponding ones of Ko (Ko, 2020) to H-valued negatively superadditive dependent random vectors.

scholarly journals A formula for hidden regular variation behavior for symmetric stable distributions

Extremes ◽  
2020 ◽  
Vol 23 (4) ◽  
pp. 667-691
Author(s):  
Malin Palö Forsström ◽  
Jeffrey E. Steif
Keyword(s):  
Power Law ◽  
Spectral Measure ◽  
Regular Variation ◽  
Stable Distributions ◽  
Decay Rates ◽  
Random Vectors ◽  
Power Law Decay ◽  
Exact Power ◽  
Hidden Regular Variation ◽  
Stable Random Vectors

Abstract We develop a formula for the power-law decay of various sets for symmetric stable random vectors in terms of how many vectors from the support of the corresponding spectral measure are needed to enter the set. One sees different decay rates in “different directions”, illustrating the phenomenon of hidden regular variation. We give several examples and obtain quite varied behavior, including sets which do not have exact power-law decay.

Orthogonal Random Vectors and the Hurwitz-Radon-Eckmann Theorem

1994 ◽  
Vol 1 (1) ◽  
pp. 99-113
Author(s):  
N. Vakhania
Keyword(s):  
Hilbert Space ◽  
Nauk Sssr ◽  
Adjoint Operator ◽  
Covariance Operator ◽  
Random Vectors ◽  
Infinite Dimensional ◽  
Infinite Dimensional Hilbert Space ◽  
And Topology ◽  
Dimensional Hilbert Space ◽  
Akad Nauk Sssr

Abstract In several different aspects of algebra and topology the following problem is of interest: find the maximal number of unitary antisymmetric operators Ui in with the property UiUj = –UjUi (i ≠ j). The solution of this problem is given by the Hurwitz-Radon-Eckmann formula. We generalize this formula in two directions: all the operators Ui must commute with a given arbitrary self-adjoint operator and H can be infinite-dimensional. Our second main result deals with the conditions for almost sure orthogonality of two random vectors taking values in a finite or infinite-dimensional Hilbert space H. Finally, both results are used to get the formula for the maximal number of pairwise almost surely orthogonal random vectors in H with the same covariance operator and each pair having a linear support in H ⊕ H. The paper is based on the results obtained jointly with N.P.Kandelaki (see [Vakhania and Kandelaki, Dokl. Akad. Nauk SSSR 294: 528-531, 1987, Dokl. Akad. Nauk SSSR 296: 265-266, 1988, Trudy Inst. Vychisl. Mat. Akad. Nauk Gruz. SSR 28: 11-37, 1988]).

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