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Excursion probability of Gaussian random fields on sphere

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TLDR
Chan and Lai as mentioned in this paper showed that the asymptotics of Piterbarg's approximation is similar to Pickands' approximation on the Euclidean space which involves pickands' constant.
Abstract
Let $X=\{X(x): x\in\mathbb{S}^N\}$ be a real-valued, centered Gaussian random field indexed on the $N$-dimensional unit sphere $\mathbb{S}^N$. Approximations to the excursion probability ${\mathbb{P}}\{\sup_{x\in\mathbb{S}^N}X(x)\ge u\}$, as $u\to\infty$, are obtained for two cases: (i) $X$ is locally isotropic and its sample functions are non-smooth and; (ii) $X$ is isotropic and its sample functions are twice differentiable. For case (i), the excursion probability can be studied by applying the results in Piterbarg (Asymptotic Methods in the Theory of Gaussian Processes and Fields (1996) Amer. Math. Soc.), Mikhaleva and Piterbarg (Theory Probab. Appl. 41 (1997) 367--379) and Chan and Lai (Ann. Probab. 34 (2006) 80--121). It is shown that the asymptotics of ${\mathbb{P}}\{\sup_{x\in\mathbb {S}^N}X(x)\ge u\}$ is similar to Pickands' approximation on the Euclidean space which involves Pickands' constant. For case (ii), we apply the expected Euler characteristic method to obtain a more precise approximation such that the error is super-exponentially small.

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A quantitative central limit theorem for the Euler–Poincaré characteristic of random spherical eigenfunctions

TL;DR: In this paper, the central limit theorem for the Euler-Poincare characteristic of excursion sets of random spherical eigenfunctions in dimension 2 was established based on a decomposition of Euler's characteristic into different Wiener-chaos components: its asymptotic behaviour is dominated by a single term corresponding to the chaotic component of order two.
Journal ArticleDOI

Stein-Malliavin Approximations for Nonlinear Functionals of Random Eigenfunctions on S^d

TL;DR: In this paper, the authors investigated Stein-Malliavin approximations for nonlinear functionals of geometric interest for random eigenfunctions on the unit d-dimensional sphere S d, d ≥ 2.
Journal ArticleDOI

Distribution of the height of local maxima of Gaussian random fields

TL;DR: Techniques from random matrix theory related to the Gaussian orthogonal ensemble are applied to compute such conditional probabilities explicitly when T is Euclidean or a sphere of arbitrary dimension.
Journal ArticleDOI

Extremes of vector-valued Gaussian processes: exact asymptotics

TL;DR: In this paper, the exact asymptotics of P ( ∃ t ∈ [ 0, T ] ∀ i = 1, …, n X i ( t ) > u ) as u → ∞, for both locally stationary X i and X i ) with a non-constant generalized variance function were derived.
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Expected Number and Height Distribution of Critical Points of Smooth Isotropic Gaussian Random Fields.

TL;DR: Formulae are obtained for the expected number and height distribution of critical points of smooth isotropic Gaussian random fields parameterized on Euclidean space or spheres of arbitrary dimension based on a characterization of the distribution of the Hessian of the Gaussian field by means of the family of Gaussian orthogonally invariant matrices.
References
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Book

Orthogonal polynomials

Gábor Szegő
Posted Content

Orthogonal Polynomials

Vilmos Totik
TL;DR: In this paper, different aspects of the theory of orthogonal polynomials of one (real or complex) variable are reviewed and orthogonality on the unit circle is not discussed.
Book

Random Fields and Geometry

TL;DR: Random Fields and Geometry as discussed by the authors is a comprehensive survey of the general theory of Gaussian random fields with a focus on geometric problems arising in the study of random fields, including continuity and boundedness, entropy and majorizing measures, Borell and Slepian inequalities.
Book

The Geometry of Random Fields

TL;DR: In this article, the authors present a survey of random fields and excursion sets and their spectral properties, including sample function regularity, sample function erraticism, and the Markov property for Gaussian fields.