Functional extreme rainfall data in Petaling Jaya by Mohamad Adnan, Noor Izyan, Adam, Mohd Bakri, Ibrahim, Noor Akma, Ishak, Mohd Yusoff, Azmai, Mohammad Noor Amal

“…This research implements two important main fields of the study which is functional data analysis and extreme value theory. The aims of this study are to convert the extreme rainfall data into functions and to propose method development for functional data analysis in extreme value theory. …”
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Invariance properties of limiting point processes and applications to clusters of extremes by Janßen Anja, Segers Johan

“…Motivated by examples from extreme value theory, but without using the theory of regularly varying time series or any assumptions about the marginal distribution, we introduce the general notion of a cluster process as a limiting point process of returns of a certain event in a time series. …”
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ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM by Weng, Tsui-Wei, Zhang, Huan, Chen, Pin-Yu, Lozano, Aurelie, Hsieh, Cho-Jui, Daniel, Luca

“…CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). …”
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State-dependence of Cenozoic thermal extremes by B. B. Cael, Philip Goodwin

“…Multi-millennial hyperthermal events, such as the Palaeocene-Eocene thermal maximum, are more likely to occur when the baseline temperature is higher, according to an application of extreme value theory to Cenozoic foraminifera proxy records…”
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Vertical structure of extreme currents in the Faroe-Bank Channel by C. Carollo, I. Astin, J. Graff

“…Therefore, it must be taken into consideration when applying the extreme value theory, not to underestimate the return level for total currents. …”
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A probabilistic analysis of wind gusts using extreme value statistics by Petra Friederichs, Martin Göber, Sabrina Bentzien, Anne Lenz, Rebekka Krampitz

“…The study points to the benefit from using extreme value theory as the most appropriate and theoretically consistent statistical model. …”
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ON EXTENSIONS OF CLEVER: A NEURAL NETWORK ROBUSTNESS EVALUATION ALGORITHM by Weng, Tsui-Wei, Zhang, Huan, Chen, Pin-Yu, Lozano, Aurelie, Hsieh, Cho-Jui, Daniel, Luca

“…CLEVER (Cross-Lipschitz Extreme Value for nEtwork Robustness) is an Extreme Value Theory (EVT) based robustness score for large-scale deep neural networks (DNNs). …”
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Comparing riskiness of exchange rate volatility using the Value at Risk and Expected Shortfall methods by Thabani Ndlovu, Delson Chikobvu

“…The risks calculated are tail-related measures, so the Extreme Value Theory is used to capture extreme risk more accurately. …”
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Study on the Peak Factor of the Wind-Induced Response of Super-High-Rise Buildings by Jun-Bo Wang, Yu Wang, Lei Wang, Shu-Guo Liang

“…Thereafter, the peak factor of wind-induced response was calculated using the peak factor method, classical extreme value theory, and the improved peak factor method. …”
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Hidden spatiotemporal sequence in transition to shear band in amorphous solids by Zeng-Yu Yang, Yun-Jiang Wang, Lan-Hong Dai

“…The hidden mechanism is then revealed with the help of extreme value theory and percolation analysis. Numerical evidence from extreme value theory indicates that dilatation is the dominant mode at the embryonic stage of the initial plastic units, as evidenced through the larger degree of dilatation localization compared with shear and rotation. …”
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Characterizing the Variability and Extremes of the Stratospheric Polar Vortices Using 2D Moment Analysis by Mitchell, D, Charlton-Perez, A, Gray, L

“…The mean state, variability, and extreme variability of the stratospheric polar vortices, with an emphasis on the Northern Hemisphere (NH) vortex, are examined using two-dimensional moment analysis and extreme value theory (EVT). The use of moments as an analysis tool gives rise to information about the vortex area, centroid latitude, aspect ratio, and kurtosis. …”

Survival Study of Extreme Record by Adam, Mohd Bakri

“…We make use of the extreme value theory for minima and utilized the facility provided by the Kaplan-Meier to develop new goodness-of-fit test method via graphical approaches.…”
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Testing the assumptions behind importance sampling. by Koopman, S, Shephard, N, Creal, D

“…In this paper we use extreme value theory to empirically assess the appropriateness of this assumption. …”

Assessing Drawdown-at-Risk in Brazilian Real Foreign Exchange Rates by Vinicius Ratton Brandi, Beatriz Vaz de Melo Mendes

“…In this work, we use distributions from extreme value theory to model the severity of drawdowns and drawups. …”
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Maritime abnormality detection using Gaussian processes by Smith, M, Reece, S, Roberts, S, Psorakis, I, Rezek, I

“…This paper introduces novelty detection techniques using a combination of Gaussian processes, extreme value theory and divergence measurement to identify anomalous behaviour in both streaming and batch data. …”

Testing the assumptions behind importance sampling. by Koopman, S, Shephard, N, Creal, D

“…In this paper we use extreme value theory to empirically assess the appropriateness of this assumption. …”

An improved dynamic programming tracking-before-detection algorithm based on LSTM network value function by Fei Song, Yong Li, Wei Cheng, Limeng Dong

“…Simulation results show that the improved architecture is able to effectively reduce the aggregation effect of a posterior value function and improve the detection and tracking ability for non-cooperative nonlinear maneuvering dim small target.AbbreviationsLSTM: Long short-term memory; DP-TBD: Dynamic programming-based tracking before detection; DBT: Detection before tracking; TBD: Tracking before detection; HT-TBD: Tracking-before-detection algorithm based on the Hough transform; PF-TBD: Tracking-before-detection algorithm based on particle filtering; RFS-TBD: Tracking-before-detection algorithm based on random finite sets; SNR: Signal-to-noise ratio; DP: Dynamic programming; EVT: Extreme value theory; EVT: Generalized extreme value theory; GLRT: Generalized likelihood ratio detection; KT: Keystone transformation; PGA: Phase gradient autofocusing; CFAR: Constant false-alarm rate; J-CA-CFAR: Joint intensity-spatial CFAR; MF: Merit function; CP-DP-TBD: Candidate plot-based DP-TBD; CIT: Coherent integration time; RNN: Recurrent neural network; CS: Current statistical; Pd: Detection probability; Pt: Tracking probability.…”
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Testing the Assumptions Behind the Use of Importance Sampling. by Koopman, S, Shephard, N

“…In this paper we propose to use extreme value theory to empirically assess the appropriateness of this assumption. …”

Point Processes in a Metric Space and Their Applications by Yuwei Zhao

“…Point processes are important in extreme value theory due to their equivalent formulations of two popular models in various applications: the block maxima models and the peak-over-threshold model. …”
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Percolation Transition for Random Walk with Non-local Movements by M Feshanjerdi, A A Saberi

“…Also, we find the universal scaling functions for the size of the largest gap and biggest cluster by the extreme value theory.…”
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