Extremes of subexponential Lévy-driven random fields in the Gumbel domain of attraction

Extremes ◽  
2021 ◽  
Author(s):  
Mads Stehr ◽  
Anders Rønn-Nielsen
Keyword(s):  
Random Fields ◽  
Domain Of Attraction

scholarly journals A Note on the Extremal Index for Space-Time Processes

2006 ◽  
Vol 43 (01) ◽  
pp. 114-126
Author(s):  
K. F. Turkman
Keyword(s):  
Random Field ◽  
Random Fields ◽  
Domain Of Attraction ◽  
Extremal Index ◽  
Space Time ◽  
Limiting Distribution ◽  
Distribution Of The Maximum ◽  
Stationary Random Field

Let {X( s , t), s = (s 1, s 2) ∈ ℝ2, t ∈ ℝ} be a stationary random field defined over a discrete lattice. In this paper, we consider a set of domain of attraction criteria giving the notion of extremal index for random fields. Together with the extremal-types theorem given by Leadbetter and Rootzen (1997), this will give a characterization of the limiting distribution of the maximum of such random fields.

scholarly journals A Note on the Extremal Index for Space-Time Processes

2006 ◽  
Vol 43 (1) ◽  
pp. 114-126 ◽  
Author(s):  
K. F. Turkman
Keyword(s):  
Random Field ◽  
Random Fields ◽  
Domain Of Attraction ◽  
Extremal Index ◽  
Space Time ◽  
Limiting Distribution ◽  
Distribution Of The Maximum ◽  
Stationary Random Field

Let {X(s, t), s = (s1, s2) ∈ ℝ2, t ∈ ℝ} be a stationary random field defined over a discrete lattice. In this paper, we consider a set of domain of attraction criteria giving the notion of extremal index for random fields. Together with the extremal-types theorem given by Leadbetter and Rootzen (1997), this will give a characterization of the limiting distribution of the maximum of such random fields.

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

PRNU-based Image Manipulation Localization with Discriminative Random Fields

2017 ◽  
Vol 2017 (7) ◽  
pp. 113-120 ◽  
Author(s):  
Sujoy Chakraborty ◽  
Matthias Kirchner
Keyword(s):  
Random Fields ◽  
Image Manipulation
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