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Journal articles on the topic 'Parametric Multivariate Extreme Value Models'

Author: Grafiati

Published: 6 September 2023

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1

Morganti, Paolo Riccardo. "Extreme Value Theory and Auction Models." Abril - Junio 2021 16, no. 2 (2021): 1–15. http://dx.doi.org/10.21919/remef.v16i2.596.

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The objective of this article is to develop a parametric approach to estimating auctions with incomplete data using Extreme Value Theory (EVT). The methodology is mainly theoretical: we first review that, when only transaction prices can be observed, the distribution of private valuations is irregularly identified. The sample bias produced by nonparametric estimators will affect all functionals of practical interest. We provide simulations for a best-case scenario and a worst-case scenario. Our results show that, compared to nonparametric approaches, the approximation of such functionals devel

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2

AghaKouchak, Amir, and Nasrin Nasrollahi. "Semi-parametric and Parametric Inference of Extreme Value Models for Rainfall Data." Water Resources Management 24, no. 6 (2009): 1229–49. http://dx.doi.org/10.1007/s11269-009-9493-3.

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Božović, Miloš. "Portfolio Tail Risk: A Multivariate Extreme Value Theory Approach." Entropy 22, no. 12 (2020): 1425. http://dx.doi.org/10.3390/e22121425.

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This paper develops a method for assessing portfolio tail risk based on extreme value theory. The technique applies separate estimations of univariate series and allows for closed-form expressions for Value at Risk and Expected Shortfall. Its forecasting ability is tested on a portfolio of U.S. stocks. The in-sample goodness-of-fit tests indicate that the proposed approach is better suited for portfolio risk modeling under extreme market movements than comparable multivariate parametric methods. Backtesting across multiple quantiles demonstrates that the model cannot be rejected at any reasona

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Guevara, C. Angelo, and Moshe E. Ben-Akiva. "Sampling of alternatives in Multivariate Extreme Value (MEV) models." Transportation Research Part B: Methodological 48 (February 2013): 31–52. http://dx.doi.org/10.1016/j.trb.2012.11.001.

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Salvadori, G., and C. De Michele. "Estimating strategies for multiparameter Multivariate Extreme Value copulas." Hydrology and Earth System Sciences 15, no. 1 (2011): 141–50. http://dx.doi.org/10.5194/hess-15-141-2011.

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Abstract. Multivariate Extreme Value models are a fundamental tool in order to assess potentially dangerous events. Exploiting recent theoretical developments in the theory of Copulas, new multiparameter models can be easily constructed. In this paper we suggest several strategies in order to estimate the parameters of the selected copula, according to different criteria: these may use a single station approach, or a cluster strategy, or exploit all the pair-wise relationships between the available gauge stations. An application to flood data is also illustrated and discussed.

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Salvadori, G., and C. De Michele. "Estimating strategies for Multiparameter Multivariate Extreme value copulas." Hydrology and Earth System Sciences Discussions 7, no. 5 (2010): 7563–90. http://dx.doi.org/10.5194/hessd-7-7563-2010.

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Abstract. Multivariate Extreme Value models are a fundamental tool in order to assess potentially dangerous events. Exploiting recent theoretical developments in the theory of Copulas, new multiparameter models can be easily constructed. In this paper we suggest several strategies in order to estimate the parameters of the selected copula, according to different criteria: these may use either a nearest neighbor or a nearest cluster approach, or exploit all the pair-wise relationships between the available gauge stations. An application to flood data is also illustrated and discussed.

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Han, Yu. "Semi-Parametric Statistical Model for Extreme Value Statistical Models and Application in Automatic Control." Applied Mechanics and Materials 680 (October 2014): 455–58. http://dx.doi.org/10.4028/www.scientific.net/amm.680.455.

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The frequency that extreme events appear in the life is low,but once it appears,the impact will be significant; many scholars have conducted in depth research and found that statistical theory of extreme value. The theory of extreme statistics plays a more and more important role in many fields such as automatic control, assembly line etc. This paper,makes an in-depth research towards the characteristics and parameter estimation of the extreme value statistical models,as well as the application,mainly analyzes the Bayes parameter estimation method of extreme value distribution,the extreme valu

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Cirillo, Pasquale, and Jürg Hüsler. "GENERALIZED EXTREME SHOCK MODELS WITH A POSSIBLY INCREASING THRESHOLD." Probability in the Engineering and Informational Sciences 25, no. 3 (2011): 419–34. http://dx.doi.org/10.1017/s0269964811000088.

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We propose a generalized extreme shock model with a possibly increasing failure threshold. Although standard models assume that the crucial threshold for the system might only decrease over time, because of weakening shocks and obsolescence, we assume that, especially at the beginning of the system's life, some strengthening shocks might increase the system tolerance to large shock. This is, for example, the case of turbines’ running-in in the field of engineering. On the basis of parametric assumptions, we provide theoretical results and derive some exact and asymptotic univariate and multiva

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Bounceur, Ahcene, Salvador Mir, Reinhardt Euler, and Kamel Beznia. "Estimation of Analog/RF Parametric Test Metrics Based on a Multivariate Extreme Value Model." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 5 (2020): 966–76. http://dx.doi.org/10.1109/tcad.2019.2907923.

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Kyselý, Jan. "A Cautionary Note on the Use of Nonparametric Bootstrap for Estimating Uncertainties in Extreme-Value Models." Journal of Applied Meteorology and Climatology 47, no. 12 (2008): 3236–51. http://dx.doi.org/10.1175/2008jamc1763.1.

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Abstract The parametric and nonparametric approaches to the bootstrap are compared as to their performance in estimating uncertainties in extreme-value models. Simulation experiments make use of several combinations of true and fitted probability distributions utilized in climatological and hydrological applications. The results demonstrate that for small to moderate sample sizes the nonparametric bootstrap should be interpreted with caution because it leads to confidence intervals that are too narrow and underestimate the real uncertainties involved in the frequency models. Although the param

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Beirlant, J., G. Matthys, and G. Dierckx. "Heavy-Tailed Distributions and Rating." ASTIN Bulletin 31, no. 1 (2001): 37–58. http://dx.doi.org/10.2143/ast.31.1.993.

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AbstractIn this paper we consider the problem raised in the Astin Bulletin (1999) by Prof. Benktander at the occasion of his 80th birthday concerning the choice of an appropriate claim size distribution in connection with reinsurance rating problems. Appropriate models for large claim distributions play a central role in this matter. We review the literature on extreme value methodology and consider its use in reinsurance. Whereas the models in extreme-value methods are non-parametric or semi-parametric of nature, practitioners often need a fully parametric model for assessing a portfolio risk

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Degen, Matthias, and Paul Embrechts. "EVT-based estimation of risk capital and convergence of high quantiles." Advances in Applied Probability 40, no. 3 (2008): 696–715. http://dx.doi.org/10.1239/aap/1222868182.

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We discuss some issues regarding the accuracy of a quantile-based estimation of risk capital. In this context, extreme value theory (EVT) emerges naturally. The paper sheds some further light on the ongoing discussion concerning the use of a semi-parametric approach like EVT and the use of specific parametric models such as the g-and-h. In particular, we discusses problems and pitfalls evolving from such parametric models when using EVT and highlight the importance of the underlying second-order tail behavior.

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Degen, Matthias, and Paul Embrechts. "EVT-based estimation of risk capital and convergence of high quantiles." Advances in Applied Probability 40, no. 03 (2008): 696–715. http://dx.doi.org/10.1017/s0001867800002755.

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We discuss some issues regarding the accuracy of a quantile-based estimation of risk capital. In this context, extreme value theory (EVT) emerges naturally. The paper sheds some further light on the ongoing discussion concerning the use of a semi-parametric approach like EVT and the use of specific parametric models such as the g-and-h. In particular, we discusses problems and pitfalls evolving from such parametric models when using EVT and highlight the importance of the underlying second-order tail behavior.

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14

önalan, ömer. "The modeling of extreme stochastic dependence using copulas and extreme value theory: case study from energy prices." Global Journal of Mathematical Analysis 5, no. 2 (2017): 29. http://dx.doi.org/10.14419/gjma.v5i2.7256.

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In this paper, we investigate the properties of tail dependence with an approach which is based on the copula models and extreme value theory to obtain a joint distribution function of extreme events and to quantify the dependence between random variables. To achieve this objective, we quantify the large co-movements between the random variables returns which are based on the data set daily quotes of exceeds the threshold value of random variables. In this study, stochastic dependence was modeled by the copulas which it provides a good approach for constructing multivariate probability distrib

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Moretti, Alba Regina, and Beatriz Vaz de Melo Mendes. "Medindo a Influência do Mercado dos EUA sobre as Interdependências Observadas na América Latina." Brazilian Review of Finance 3, no. 1 (2005): 123. http://dx.doi.org/10.12660/rbfin.v3n1.2005.1147.

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The modeling of the extremal dependence structure can be made through parametric models classified in two families: Logistic and Mixed, which contain the symmetric and asymmetric models. The bivariate models are very useful in practical applications on the extreme value theory, in particular in a financial area. Considering the strong influence of the North American market on other financial markets, we investigate how does the dependence structure among the Latin American markets change after filtering the influence of the North American market. To remove that influence, we carry on a polynom

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Basterfield, David, and Thomas Bundt. "Multivariate Stable Distributions and Value at Risk: The Case of the Asian Currency Crisis." Journal of Finance Issues 5, no. 1 (2007): 187–95. http://dx.doi.org/10.58886/jfi.v5i1.2601.

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This paper explores practical applications of multivariate stable distributions to value at risk modeling during the Asian currency crisis. We fit multivariate stable distributions to daily foreign exchange rate data 1996 through 1998 for six Asian currencies using a rolling estimation procedure and backtest daily marginal and conditional probabilities under 95% and 99% value at risk nulls. We also examine gains in value at risk accuracy from using multivariate stable distributions relative to univariate benchmarks such as generalized autoregressive conditional heteroskedasticity or univariate

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17

Cardell, N. Scott. "Variance Components Structures for the Extreme-Value and Logistic Distributions with Application to Models of Heterogeneity." Econometric Theory 13, no. 2 (1997): 185–213. http://dx.doi.org/10.1017/s0266466600005727.

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Two new classes of probability distributions are introduced that radically simplify the process of developing variance components structures for extremevalue and logistic distributions. When one of these new variates is added to an extreme-value (logistic) variate, the resulting distribution is also extreme value (logistic). Thus, quite complicated variance structures can be generated by recursively adding components having this new distribution, and the result will retain a marginal extreme-value (logistic) distribution. It is demonstrated that the computational simplicity of extreme-value er

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Shortridge, Julie E., Seth D. Guikema, and Benjamin F. Zaitchik. "Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds." Hydrology and Earth System Sciences 20, no. 7 (2016): 2611–28. http://dx.doi.org/10.5194/hess-20-2611-2016.

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Abstract. In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based models are limited. However, the majority of research has focused on a small number of methods, such as artificial neural networks, despite the development of multiple other approaches for non-parametric regression in recent years. Furthermore, this work has often evaluated model performance based on predictive accuracy alone, while not cons

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19

Arun, Ashutosh, Md Mazharul Haque, Ashish Bhaskar, and Simon Washington. "Transferability of multivariate extreme value models for safety assessment by applying artificial intelligence-based video analytics." Accident Analysis & Prevention 170 (June 2022): 106644. http://dx.doi.org/10.1016/j.aap.2022.106644.

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20

Taylor, Stephen. "Clustering Financial Return Distributions Using the Fisher Information Metric." Entropy 21, no. 2 (2019): 110. http://dx.doi.org/10.3390/e21020110.

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Information geometry provides a correspondence between differential geometry and statistics through the Fisher information matrix. In particular, given two models from the same parametric family of distributions, one can define the distance between these models as the length of the geodesic connecting them in a Riemannian manifold whose metric is given by the model’s Fisher information matrix. One limitation that has hindered the adoption of this similarity measure in practical applications is that the Fisher distance is typically difficult to compute in a robust manner. We review such complic

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21

Louisor, Jessie, Jérémy Rohmer, Thomas Bulteau, et al. "Deriving the 100-Year Total Water Level around the Coast of Corsica by Combining Trivariate Extreme Value Analysis and Coastal Hydrodynamic Models." Journal of Marine Science and Engineering 9, no. 12 (2021): 1347. http://dx.doi.org/10.3390/jmse9121347.

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As low-lying coastal areas can be impacted by flooding caused by dynamic components that are dependent on each other (wind, waves, water levels—tide, atmospheric surge, currents), the analysis of the return period of a single component is not representative of the return period of the total water level at the coast. It is important to assess a joint return period of all the components. Based on a semiparametric multivariate extreme value analysis, we determined the joint probabilities that significant wave heights (Hs), wind intensity at 10 m above the ground (U), and still water level (SWL) e

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Bengtsson, A., and C. Nilsson. "Extreme value modelling of storm damage in Swedish forests." Natural Hazards and Earth System Sciences 7, no. 5 (2007): 515–21. http://dx.doi.org/10.5194/nhess-7-515-2007.

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Abstract. Forests cover about 56% of the land area in Sweden and forest damage due to strong winds has been a recurring problem. In this paper we analyse recorded storm damage in Swedish forests for the years 1965–2007. During the period 48 individual storm events with a total damage of 164 Mm³ have been reported with the severe storm on 8 to 9 January 2005, as the worst with 70 Mm³ damaged forest. For the analysis, storm damage data has been normalised to account for the increase in total forest volume over the period. We show that, within the framework of statistical extreme value theory, a

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23

Waheed, Saddam Q., Neil S. Grigg, and Jorge A. Ramirez. "Development of a Parametric Regional Multivariate Statistical Weather Generator for Risk Assessment Studies in Areas with Limited Data Availability." Climate 8, no. 8 (2020): 93. http://dx.doi.org/10.3390/cli8080093.

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Risk analysis of water resources systems can use statistical weather generators coupled with hydrologic models to examine scenarios of extreme events caused by climate change. These require multivariate, multi-site models that mimic the spatial, temporal, and cross correlations of observed data. This study developed a statistical weather generator to facilitate bottom-up approaches to assess the impact of climate change on water resources systems for cases of limited data. While existing weather generator models have impressive features, this study suggested a simple weather generator which is

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Budiarti, Retno, Kumala Intansari, I. Gusti Putu Purnaba, and Fendy Septyanto. "Modelling Dependencies of Stock Indices During Covid-19 Pandemic by Extreme-Value Copula." JTAM (Jurnal Teori dan Aplikasi Matematika) 7, no. 3 (2023): 805. http://dx.doi.org/10.31764/jtam.v7i3.15109.

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Quantifying dependence among variables is the core of all modelling efforts in financial models. In the recent years, copula was introduced to model the dependence structure among financial assets return, and its application developed fast. A large number of studies on copula have been performed, but the study of multivariate extremes related with copulas was quite behind in comparison with the research on copulas. The COVID-19 pandemic is an extreme event that has caused the collapse of various economic activities which resulted in the decline of stock prices. The modelling of extreme events

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He, Q., Y. J. Zheng, C. L. Zhang, and H. Y. Wang. "MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern." Complexity 2020 (October 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8846608.

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Currently, multivariate time series anomaly detection has made great progress in many fields and occupied an important position. The common limitation of many related studies is that there is only temporal pattern without capturing the relationship between variables and the loss of information leads to false warnings. Our article proposes an unsupervised multivariate time series anomaly detection. In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root

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Arthur, William C. "A statistical–parametric model of tropical cyclones for hazard assessment." Natural Hazards and Earth System Sciences 21, no. 3 (2021): 893–916. http://dx.doi.org/10.5194/nhess-21-893-2021.

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Abstract. We present the formulation of an open-source, statistical–parametric model of tropical cyclones (TCs) for use in hazard and risk assessment applications. The model derives statistical relations for TC behaviour (genesis rate and location, intensity, speed and direction of translation) from best-track datasets, then uses these relations to create a synthetic catalogue based on stochastic sampling, representing many thousands of years of activity. A parametric wind field, based on radial profiles and boundary layer models, is applied to each event in the catalogue that is then used to

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Yang, Wei, Liping Zhang, Lijie Shan, Xinchi Chen, and Shaodan Chen. "Response of Extreme Hydrological Events to Climate Change in the Water Source Area for the Middle Route of South-to-North Water Diversion Project." Advances in Meteorology 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/2486928.

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As the water source area for the middle route of China’s South-to-North Water Diversion Project, the upper Hanjiang basin is of central concern for future management of the country’s water resources. The upper Hanjiang is also one of the most flood-prone rivers in China. This paper explores the process of extreme floods by using multivariate analysis to characterize flood and precipitation event data in combination, for historical data and simulated data from global climate models. The results suggested that the generalized extreme value and Gamma models better simulated the extreme precipitat

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Ma, Jie, Xin Ye, and Abdul Rawoof Pinjari. "Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (2019): 145–56. http://dx.doi.org/10.1177/0361198119842819.

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The multiple discrete-continuous generalized extreme value (MDCGEV) model has been derived from multivariate extreme value (MEV)-based stochastic specifications to relax the independence assumption in the multiple discrete-continuous extreme value (MDCEV) model. It is analogous to the situation where a generalized extreme value (GEV) model relaxes the same assumption in a multinomial logit (MNL) model. However, unlike the case of single discrete choice model where substitution patterns can be understood based on elasticity expressions for a change in the value of an explanatory variable, the M

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Simpson, E. S., J. L. Wadsworth, and J. A. Tawn. "Determining the dependence structure of multivariate extremes." Biometrika 107, no. 3 (2020): 513–32. http://dx.doi.org/10.1093/biomet/asaa018.

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Summary In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies in determining which subsets of variables can take their largest values simultaneously while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of nonstandard cones, and provides a new set of indices that reveal aspects of the extremal dependence structure not available through existing measures of dependence. We derive theoretical pr

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Glasgow, Garrett. "Mixed Logit Models for Multiparty Elections." Political Analysis 9, no. 2 (2001): 116–36. http://dx.doi.org/10.1093/oxfordjournals.pan.a004867.

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Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choi

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Galiatsatou, Panagiota, Christos Makris, Yannis Krestenitis, and Panagiotis Prinos. "Nonstationary Extreme Value Analysis of Nearshore Sea-State Parameters under the Effects of Climate Change: Application to the Greek Coastal Zone and Port Structures." Journal of Marine Science and Engineering 9, no. 8 (2021): 817. http://dx.doi.org/10.3390/jmse9080817.

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In the present work, a methodological framework, based on nonstationary extreme value analysis of nearshore sea-state parameters, is proposed for the identification of climate change impacts on coastal zone and port defense structures. The applications refer to the estimation of coastal hazards on characteristic Mediterranean microtidal littoral zones and the calculation of failure probabilities of typical rubble mound breakwaters in Greek ports. The proposed methodology hinges on the extraction of extreme wave characteristics and sea levels due to storm events affecting the coast, a nonstatio

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Olinda, R. A., J. Blanchet, C. A. C. dos Santos, V. A. Ozaki, and P. J. Ribeiro Jr. "Spatial extremes modeling applied to extreme precipitation data in the state of Paraná." Hydrology and Earth System Sciences Discussions 11, no. 11 (2014): 12731–64. http://dx.doi.org/10.5194/hessd-11-12731-2014.

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Abstract. Most of the mathematical models developed for rare events are based on probabilistic models for extremes. Although the tools for statistical modeling of univariate and multivariate extremes are well developed, the extension of these tools to model spatial extremes includes an area of very active research nowadays. A natural approach to such a modeling is the theory of extreme spatial and the max-stable process, characterized by the extension of infinite dimensions of multivariate extreme value theory, and making it possible then to incorporate the existing correlation functions in ge

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Steinheuer, Julian, and Petra Friederichs. "Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory." Nonlinear Processes in Geophysics 27, no. 2 (2020): 239–52. http://dx.doi.org/10.5194/npg-27-239-2020.

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Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanal

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Suleev, Bakhtiar, and Bakyt Kurmasheva. "THEORETICAL FOUNDATIONS OF THE DEVELOPMENT OF A BULLDOZER DESIGN THROUGH THE USE OF MULTIVARIATE PARAMETRIC ANALYSIS." Вестник КазАТК 126, no. 3 (2023): 74–81. http://dx.doi.org/10.52167/1609-1817-2023-126-3-74-81.

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The article presents the scope, purpose and design of the bulldozer. The main brands of bulldozers used in our republic are given. Their main characteristics are considered, such as: weight, power, blade width and blade height. As a result of the analysis of parametric information, the standard deviation, average value, coefficient of variation, asymmetry and kurtosis were determined. A correlation matrix has been compiled that describes the relationship between the parameters of the machines. Based on the results obtained, a cluster analysis was carried out in the StatGraphics Centurion softw

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Pipan, Tanja, Mary C. Christman, and David C. Culver. "Abiotic Community Constraints in Extreme Environments: Epikarst Copepods as a Model System." Diversity 12, no. 7 (2020): 269. http://dx.doi.org/10.3390/d12070269.

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The general hypothesis that the overall presence or absence of one or more species in an extreme habitat is determined by physico-chemical factors was investigated using epikarst copepod communities as a model system, an example of an extreme environment with specialized, often rare species. The relationship between the presence or absence of epikarst copepods from drips in six Slovenian caves and 12 physico-chemical factors (temperature, conductivity, pH, Ca2+, Na+, K+, Mg2+, NH4+, and Cl−, NO2−, NO3−, and SO42−) was explored. Statistical analyses included principal components analysis, logis

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Jin, Xisong, and Thorsten Lehnert. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas." Dependence Modeling 6, no. 1 (2018): 19–46. http://dx.doi.org/10.1515/demo-2018-0002.

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Abstract Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) for passive portfolios and dynamic optimal portfolios

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MacAfee, Allan W., and Samuel W. K. Wong. "Extreme Value Analysis of Tropical Cyclone Trapped-Fetch Waves." Journal of Applied Meteorology and Climatology 46, no. 10 (2007): 1501–22. http://dx.doi.org/10.1175/jam2555.1.

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Abstract Many of the extreme ocean wave events generated by tropical cyclones (TCs) can be explained by examining one component of the spectral wave field, the trapped-fetch wave (TFW). Using a Lagrangian TFW model, a parametric model representation of the local TC wind fields, and the National Hurricane Center’s hurricane database archive, a dataset of TFWs was created from all TCs in the Atlantic Ocean, Gulf of Mexico, and Caribbean Sea from 1851 to 2005. The wave height at each hourly position along a TFW trajectory was sorted into 2° × 2° latitude–longitude grid squares. Five grid squares

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Hammami, Hela, Julie Carreau, Luc Neppel, Sadok Elasmi, and Haifa Feki. "Smooth Spatial Modeling of Extreme Mediterranean Precipitation." Water 14, no. 22 (2022): 3782. http://dx.doi.org/10.3390/w14223782.

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Extreme precipitation events can lead to disastrous floods, which are the most significant natural hazards in the Mediterranean regions. Therefore, a proper characterization of these events is crucial. Extreme events defined as annual maxima can be modeled with the generalized extreme value (GEV) distribution. Owing to spatial heterogeneity, the distribution of extremes is non-stationary in space. To take non-stationarity into account, the parameters of the GEV distribution can be viewed as functions of covariates that convey spatial information. Such functions may be implemented as a generali

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Hitz, Adrien, and Robin Evans. "One-component regular variation and graphical modeling of extremes." Journal of Applied Probability 53, no. 3 (2016): 733–46. http://dx.doi.org/10.1017/jpr.2016.37.

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AbstractThe problem of inferring the distribution of a random vector given that its norm is large requires modeling a homogeneous limiting density. We suggest an approach based on graphical models which is suitable for high-dimensional vectors. We introduce the notion of one-component regular variation to describe a function that is regularly varying in its first component. We extend the representation and Karamata's theorem to one-component regularly varying functions, probability distributions and densities, and explain why these results are fundamental in multivariate extreme-value theory.

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Sun, Xudong, Mingxing Zhou, and Yize Sun. "Spectroscopy quantitative analysis cotton content of blend fabrics." International Journal of Clothing Science and Technology 28, no. 1 (2016): 65–76. http://dx.doi.org/10.1108/ijcst-07-2015-0076.

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Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal mod

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Satyanarayana Tani and Andreas Gobiet. "Quantile mapping for improving precipitation extremes from regional climate models." Journal of Agrometeorology 21, no. 4 (2021): 434–43. http://dx.doi.org/10.54386/jam.v21i4.278.

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 The potential of quantile mapping (QM) as a tool for bias correction of precipitation extremes simulated by regional climate models (RCMs) is investigated in this study. We developed an extended version of QM to improve the quality of bias-corrected extreme precipitation events. The extended version aims to exploit the advantages of both non-parametric methods and extreme value theory. We evaluated QM by applying it to a small ensemble of hindcast simulations, performed with RCMs at six different locations in Europe. We examined the quality of both raw and bias-corrected simulations of

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Silva, Renato Santos, and Fernando Ferraz Nascimento. "Extreme Value Theory Applied to r Largest Order Statistics Under the Bayesian Approach." Revista Colombiana de Estadística 42, no. 2 (2019): 143–66. http://dx.doi.org/10.15446/rce.v42n2.70271.

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Extreme Value Theory (EVT) is an important tool to predict efficient gains and losses. Its main areas of analyses are economic and environmental. Initially, for that form of event, it was developed the use of patterns of parametric distribution such as Normal and Gamma. However, economic and environmental data presents, in most cases, a heavy-tailed distribution, in contrast to those distributions. Thus, it was faced a great difficult to frame extreme events. Furthermore, it was almost impossible to use conventional models, making predictions about non-observed events, which exceed the maximum

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Benito Muela, Sonia, Carmen López-Martin, and Raquel Arguedas-Sanz. "A comparison of market risk measures from a twofold perspective: accurate and loss function." ACRN Journal of Finance and Risk Perspectives 11, no. 1 (2023): 79–104. http://dx.doi.org/10.35944/jofrp.2022.11.1.005.

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Under the new regulation based on Basel solvency framework, known as Basel III and Basel IV, financial institutions must calculate the market risk capital requirements based on the Expected Shortfall (ES) measure, replacing the Value at Risk (VaR) measure. In the financial literature, there are many papers dedicated to compare VaR approaches but there are few studies focusing in comparing ES approaches. To cover this gap, we have carried out a comprenhensive comparative of VaR and ES models applied to IBEX-35 stock index. The comparison has been carried out from a twofold perspective: accurate

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Sun, Xudong, and Ke Zhu. "Spectral dimensionality reduction for quantitative analysis of cotton content of blend fabrics." International Journal of Clothing Science and Technology 31, no. 3 (2019): 326–38. http://dx.doi.org/10.1108/ijcst-07-2018-0091.

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Purpose The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics. Design/methodology/approach In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were emplo

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Melina, Sukono, Herlina Napitupulu, and Norizan Mohamed. "A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review." Risks 11, no. 3 (2023): 60. http://dx.doi.org/10.3390/risks11030060.

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The COVID-19 pandemic has been an extraordinary event, the type of event that rarely occurs but that has major impacts on the stock market. The pandemic has created high volatility and caused extreme fluctuations in the stock market. The stock market can be characterized as either linear or nonlinear. One method that can detect extreme fluctuations is extreme value theory (EVT). This study employed a semisystematic literature review on the use of the EVT method to estimate investment risk in the stock market. The literature used was selected by applying the preferred reporting items for system

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Rohmer, Jeremy, Pierre Gehl, Marine Marcilhac-Fradin, Yves Guigueno, Nadia Rahni, and Julien Clément. "Non-stationary extreme value analysis applied to seismic fragility assessment for nuclear safety analysis." Natural Hazards and Earth System Sciences 20, no. 5 (2020): 1267–85. http://dx.doi.org/10.5194/nhess-20-1267-2020.

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Abstract. Fragility curves (FCs) are key tools for seismic probabilistic safety assessments that are performed at the level of the nuclear power plant (NPP). These statistical methods relate the probabilistic seismic hazard loading at the given site to the required performance of the NPP safety functions. In the present study, we investigate how the tools of non-stationary extreme value analysis can be used to model in a flexible manner the tail behaviour of the engineering demand parameter as a function of the considered intensity measure. We focus the analysis on the dynamic response of an a

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Gioia, Andrea, Maria Francesca Bruno, Vincenzo Totaro, and Vito Iacobellis. "Parametric Assessment of Trend Test Power in a Changing Environment." Sustainability 12, no. 9 (2020): 3889. http://dx.doi.org/10.3390/su12093889.

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In the context of climate and environmental change assessment, the use of probabilistic models in which the parameters of a given distribution may vary in accordance with time has reinforced the need for appropriate procedures to recognize the “statistical significance” of trends in data series arising from stochastic processes. This paper introduces a parametric methodology, which exploits a measure based on the Akaike Information Criterion (AICΔ), and a Rescaled version of the Generalized Extreme Value distribution, in which a linear deterministic trend in the position parameter is accounted

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Naveau, Philippe, Alexis Hannart, and Aurélien Ribes. "Statistical Methods for Extreme Event Attribution in Climate Science." Annual Review of Statistics and Its Application 7, no. 1 (2020): 89–110. http://dx.doi.org/10.1146/annurev-statistics-031219-041314.

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Changes in the Earth's climate have been increasingly observed. Assessing the likelihood that each of these changes has been caused by human influence is important for decision making on mitigation and adaptation policy. Because of their large societal and economic impacts, extreme events have garnered much media attention—have they become more frequent and more intense, and if so, why? To answer such questions, extreme event attribution (EEA) tries to estimate extreme event likelihoods under different scenarios. Over the past decade, statistical methods and experimental designs based on numer

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Hassouneh, Islam, Teresa Serra, and Štefan Bojnec. "Nonlinearities in the Slovenian apple price transmission." British Food Journal 117, no. 1 (2015): 461–78. http://dx.doi.org/10.1108/bfj-03-2014-0109.

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Purpose – The purpose of this paper is to assess price linkages and patterns of transmission among producer and consumer markets for apple in Slovenia. Design/methodology/approach – Non-linear error correction models are applied. Non-linearities are allowed by means of threshold and multivariate local linear regression estimation techniques. Monthly prices over the period 2000-2011 are used in the empirical application. Findings – Both techniques provide evidence of non-linearities in price adjustments. Findings suggest that producer and consumer prices tend to increase rather than decrease. R

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Shortridge, J. E., S. D. Guikema, and B. F. Zaitchik. "Empirical streamflow simulation for water resource management in data-scarce seasonal watersheds." Hydrology and Earth System Sciences Discussions 12, no. 10 (2015): 11083–127. http://dx.doi.org/10.5194/hessd-12-11083-2015.

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Abstract. In the past decade, certain methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support process-based models is limited. However, the majority of research has focused on a small number of methods, such as artificial neural networks, despite the development of multiple other approaches for non-parametric regression in recent years. Furthermore, this work has generally evaluated model performance based on predictive accuracy alone, while not considerin

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