scholarly journals Spatial deformation for nonstationary extremal dependence

2021 ◽  
Author(s):  
Jordan Richards ◽  
Jennifer L. Wadsworth
Keyword(s):  
Extremal Dependence ◽  
Spatial Deformation

Nonuniform Spatial Deformation of Light Fields by Locally Linear Transformations

2017 ◽  
Vol 36 (4) ◽  
pp. 1
Author(s):  
Clemens Birklbauer ◽  
David C. Schedl ◽  
Oliver Bimber
Keyword(s):  
Light Fields ◽  
Linear Transformations ◽  
Spatial Deformation ◽  
Locally Linear

scholarly journals Estimation of Spatial Deformation for Nonstationary Processes via Variogram Alignment

Technometrics ◽  
2021 ◽  
pp. 1-14
Author(s):  
Ghulam A. Qadir ◽  
Ying Sun ◽  
Sebastian Kurtek
Keyword(s):  
Nonstationary Processes ◽  
Spatial Deformation

scholarly journals Vulnerability Assessment of Buildings due to Land Subsidence Using InSAR Data in the Ancient Historical City of Pistoia (Italy)

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2749 ◽  
Author(s):  
Pablo Ezquerro ◽  
Matteo Del Soldato ◽  
Lorenzo Solari ◽  
Roberto Tomás ◽  
Federico Raspini ◽  
...  
Keyword(s):  
Fragility Curves ◽  
Central Italy ◽  
Sar Interferometry ◽  
Ground Subsidence ◽  
Public And Private ◽  
Radar Images ◽  
Spatial Deformation ◽  
Damage Probability ◽  
Medium Resolution ◽  
Vertical Displacements

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

Assessing extremal dependence of environmental spatial fields

2008 ◽  
Vol 19 (2) ◽  
pp. 163-182 ◽  
Author(s):  
L. Bel ◽  
J. N. Bacro ◽  
Ch. Lantuéjoul
Keyword(s):  
Extremal Dependence ◽  
Spatial Fields

Investigation of elastoplastic effects of cables under large spatial deformation

PAMM ◽  
2015 ◽  
Vol 15 (1) ◽  
pp. 185-186 ◽  
Author(s):  
Vanessa Dörlich ◽  
Stefan Diebels ◽  
Joachim Linn
Keyword(s):  
Spatial Deformation

Spatial deformation of massive nonuniform solids of revolution. Part II

1970 ◽  
Vol 2 (11) ◽  
pp. 1141-1145 ◽  
Author(s):  
S. D. Bobritskaya ◽  
A. L. Kvitka
Keyword(s):  
Spatial Deformation ◽  
Solids Of Revolution

Markets liquidity risk under extremal dependence: Analysis with VaRs methods

2012 ◽  
Vol 29 (5) ◽  
pp. 1830-1836 ◽  
Author(s):  
Awatef Ourir ◽  
Wafa Snoussi
Keyword(s):  
Liquidity Risk ◽  
Dependence Analysis ◽  
Extremal Dependence

ON THE EXTREMAL DEPENDENCE COEFFICIENTS OF GAUSSIAN DISTRIBUTIONS

Vestnik NSUEM ◽  
2021 ◽  
pp. 161-167
Author(s):  
S. E. Khrushchev
Keyword(s):  
Close Correlation ◽  
Gaussian Copula ◽  
Marginal Distributions ◽  
Gaussian Distributions ◽  
Extremal Dependence ◽  
Zero Values ◽  
Coronavirus Infection ◽  
The Russian Federation ◽  
The One ◽  
The Relationship

The paper considers a way to represent the relationship between indicators in the form of copulas. Copulas are popular mathematical tools. This is due to the fact that, on the one hand, the marginal distributions of indicators are divided in the copulas, and on the other hand, the structure of the relationship between these marginal distributions is divided, which makes it  possible to very effectively study the connections that arise in real  populations. Special attention in the work is paid to extremal dependence coefficients - important numerical characteristics of the connection in conditions of extreme small or extremely large values of indicators. It is shown that even under conditions of close correlation between the indices for a two-dimensional Gaussian distribution, the lower and upper coefficients of the extreme dependence take zero values. This indicates the impossibility of predicting the values of one indicator when fixing too small or too large values of another indicator. This work shows that the relationship between the number of COVID-19 coronavirus infections per 100,000 people and the number of deaths from COVID-19 coronavirus infection per 100,000 people in the regions of the Russian Federation can be represented in the form of a Gaussian copula.

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