Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAsymptotic behaviors of bivariate Gaussian powered extremes
60
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
In this paper, joint asymptotics of powered maxima for a triangular array of bivariate powered Gaussian random vectors are considered. Under the Husler-Reiss condition, limiting distributions of powered maxima are derived. Furthermore, the second-order expansions of the joint distributions of powered maxima are established under the refined Husler-Reiss condition.
rate research
Read More
We derive exact asymptotics of $$mathbb{P}left(sup_{tin mathcal{A}}X(t)>uright), ~text{as}~ utoinfty,$$ for a centered Gaussian field $X(t),~tin mathcal{A}subsetmathbb{R}^n$, $n>1$ with continuous sample paths a.s. and general dependence structure, for which $arg max_{tin {mathcal{A}}} Var(X(t))$ is a Jordan set with finite and positive Lebesque measure of dimension $kleq n$. Our findings are applied to deriving the asymptotics of tail probabilities related to performance tables and dependent chi processes.
In this paper, we study a random field constructed from the two-dimensional Gaussian free field (GFF) by modifying the variance along the scales in the neighborhood of each point. The construction can be seen as a local martingale transform and is akin to the time-inhomogeneous branching random walk. In the case where the variance takes finitely many values, we compute the first order of the maximum and the log-number of high points. These quantities were obtained by Bolthausen, Deuschel and Giacomin (2001) and Daviaud (2006) when the variance is constant on all scales. The proof relies on a truncated second moment method proposed by Kistler (2015), which streamlines the proof of the previous results. We also discuss possible extensions of the construction to the continuous GFF.
In this paper, joint limit distributions of maxima and minima on independent and non-identically distributed bivariate Gaussian triangular arrays is derived as the correlation coefficient of $i$th vector of given $n$th row is the function of $i/n$. Furthermore, second-order expansions of joint distributions of maxima and minima are established if the correlation function satisfies some regular conditions.
This paper considers the asymptotic distribution of the longest edge of the minimal spanning tree and nearest neighbor graph on X_1,...,X_{N_n} where X_1,X_2,... are i.i.d. in Re^2 with distribution F and N_n is independent of the X_i and satisfies N_n/nto_p1. A new approach based on spatial blocking and a locally orthogonal coordinate system is developed to treat cases for which F has unbounded support. The general results are applied to a number of special cases, including elliptically contoured distributions, distributions with independent Weibull-like margins and distributions with parallel level curves.
Consider a $n times n$ matrix from the Gaussian Unitary Ensemble (GUE). Given a finite collection of bounded disjoint real Borel sets $(Delta_{i,n}, 1leq ileq p)$, properly rescaled, and eventually included in any neighbourhood of the support of Wigners semi-circle law, we prove that the related counting measures $({mathcal N}_n(Delta_{i,n}), 1leq ileq p)$, where ${mathcal N}_n(Delta)$ represents the number of eigenvalues within $Delta$, are asymptotically independent as the size $n$ goes to infinity, $p$ being fixed. As a consequence, we prove that the largest and smallest eigenvalues, properly centered and rescaled, are asymptotically independent; we finally describe the fluctuations of the condition number of a matrix from the GUE.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
