Relevant bibliographies by topics / Generalized extreme value

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Generalized extreme value'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Generalized extreme value"

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Bali, Turan G. "The generalized extreme value distribution." Economics Letters 79, no. 3 (2003): 423–27. http://dx.doi.org/10.1016/s0165-1765(03)00035-1.

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Deetae, N., P. Khamrot, and K. Jampachaisri. "Modelling Extreme Rainfall using Extended Generalized Extreme Value Distribution." International Journal of Analysis and Applications 23 (March 17, 2025): 73. https://doi.org/10.28924/2291-8639-23-2025-73.

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This study assesses the performance of extended generalized extreme value (GEV) distribution based on Kumaraswamy generalized extreme value (KumGEV) distribution using the maximum likelihood estimates on extreme rainfall data obtained from a weather station in Phitsanulok province, a total of 408 months during January 1987 to December 2021. The findings indicate that the KumGEV distribution provides a better fit than the traditional GEV distribution, with estimated parameters µ = 41.4966 (SE = 0.6015), σ = 8.9467 (SE = 0.0797), ξ = −0.0502 (SE = 0.0308), a = 0.0310 (SE = 0.0060), and b = 0.273
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Kartik, Sethi., and P. Chakrabarty Siddhartha. "Modeling Premiums of Non-life Insurance Companies in India." Journal of Innovation Sciences and Sustainable Technologies 1, no. 3 (2021): 221–37. https://doi.org/10.0608/JISST.2022214863.

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We undertake an empirical analysis for the premium data of non-life insurance companies operating in India, in the paradigm of fitting the data for the parametric distribution of Log-normal and the extreme value-based distributions of Generalized Extreme Value and Generalized Pareto. The best fit to the data for ten companies considered is obtained for the Generalized Extreme Value distribution.
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Schellander, Harald, Alexander Lieb, and Tobias Hell. "Error Structure of Metastatistical and Generalized Extreme Value Distributions for Modeling Extreme Rainfall in Austria." Earth and Space Science 6, no. 9 (2019): 1616–32. https://doi.org/10.1029/2019EA000557.

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Incorrect estimation of extreme values of daily precipitation can have severe consequences in hydrological and engineering applications. Recent advances in the study of extreme precipitation have shown that the Metastatistical Extreme Value Distribution (MEV) is superior to the Generalized Extreme Value Distribution (GEV) whenever the length of the available record is small compared to the average recurrence time. This paper provides a detailed examination of the relative performance of MEV and GEV for both point estimates and spatial modeling. An analysis for a large number of sample years an
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Cook, Nicholas John. "Reliability of Extreme Wind Speeds Predicted by Extreme-Value Analysis." Meteorology 2, no. 3 (2023): 344–67. http://dx.doi.org/10.3390/meteorology2030021.

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The reliability of extreme wind speed predictions at large mean recurrence intervals (MRI) is assessed by bootstrapping samples from representative known distributions. The classical asymptotic generalized extreme value distribution (GEV) and the generalized Pareto (GPD) distribution are compared with a contemporary sub-asymptotic Gumbel distribution that accounts for incomplete convergence to the correct asymptote. The sub-asymptotic model is implemented through a modified Gringorten method for epoch maxima and through the XIMIS method for peak-over-threshold values. The mean bias error is sh
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Sung, Yong Kyu, and Joong K. Sohn. "Prediction of extreme rainfall with a generalized extreme value distribution." Journal of the Korean Data and Information Science Society 24, no. 4 (2013): 857–65. http://dx.doi.org/10.7465/jkdi.2013.24.4.857.

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Alaswed, H. A. "Modeling of Extreme Temperature Using min-Generalized Extreme Value Distribution." Scholars Journal of Physics, Mathematics and Statistics 11, no. 02 (2024): 18–26. http://dx.doi.org/10.36347/sjpms.2024.v11i02.001.

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Extreme value theory (EVT) is one of major importance in many fields of applications where extreme values may appear and have detrimental effects and finally Generalized Extreme Value (GEV) distribution model is found as the best fitted distribution model. Extreme minimum temperature using 40 years of data is studied. Minimum of four different time periods (monthly, quarterly, half yearly and yearly) are fitted to the minimum Generalized Extreme Value (MGEV) distribution. The first objective of this study is to describe and model the behaviour of extreme minimum temperature in Sebha by using t
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Razi Ghalavand, Mahin, Manuchehr Farajzadeh, and Yousef Ghavidel Rahimi. "Modeling Return Levels of Non-Stationary Rainfall Extremes Due to Climate Change." Atmosphere 16, no. 2 (2025): 136. https://doi.org/10.3390/atmos16020136.

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Global warming increases evaporation and atmospheric water vapor, leading to more extreme events in both spatial and temporal domains. This study conducts a non-stationary extreme value analysis of the annual daily maximum at 36 meteorological stations over Iran from 1960 to 2021. We applied stationary and non-stationary Generalized Extreme Value (GEV) models within a Bayesian framework to estimate return levels for rainfall extremes, along with 90% confidence intervals. Our findings indicate that non-stationary models are not prominently evident based on AIC at most stations; however, non-sta
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Fry, Tim R. L., and Mark N. Harris. "THE DOGIT ORDERED GENERALIZED EXTREME VALUE MODEL." Australian New Zealand Journal of Statistics 47, no. 4 (2005): 531–42. http://dx.doi.org/10.1111/j.1467-842x.2005.00414.x.

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Nasri-Roudsari, Dirk. "Extreme value theory of generalized order statistics." Journal of Statistical Planning and Inference 55, no. 3 (1996): 281–97. http://dx.doi.org/10.1016/s0378-3758(95)00200-6.

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Dissertations / Theses on the topic "Generalized extreme value"

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Han, Zhongxian. "Actuarial modelling of extremal events using transformed generalized extreme value distributions and generalized pareto distributions." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1061227080.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains x, 81 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Bostwick Wyman, Dept. of Mathematics. Includes bibliographical references (p. 80-81).
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Alentorn, Amadeo. "Option pricing with the Generalized Extreme Value distribution and applications." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437818.

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Milton, John Calvin. "Generalized extreme value and mixed logit models : empirical applications to vehicle accident severities /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/10152.

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Padoan, Simone. "Computational methods for complex problems in extreme value theory." Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3427194.

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Rare events are part of the real world but inevitably environmental extreme events may have a massive impact on everyday life. We are familiar, for example, with the consequences and damage caused by hurricanes and floods etc. Consequently, there is considerable attention in studying, understanding and predicting the nature of such phenomena and the problems caused by them, not least because of the possible link between extreme climate events and global warming or climate change. Thus the study of extreme events has become ever more important, both in terms of probabilistic and statistical re
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Shykhmanter, Dmytro. "Modeling Extreme Values." Master's thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-199737.

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Modeling of extreme events is a challenging statistical task. Firstly, there is always a limit number of observations and secondly therefore no experience to back test the result. One way of estimating higher quantiles is to fit one of theoretical distributions to the data and extrapolate to the tail. The shortcoming of this approach is that the estimate of the tail is based on the observations in the center of distribution. Alternative approach to this problem is based on idea to split the data into two sub-populations and model body of the distribution separately from the tail. This methodol
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Ekengren, Jens. "Large and rare : An extreme values approach to estimating the distribution of large defects in high-performance steels." Doctoral thesis, Karlstads universitet, Avdelningen för maskin- och materialteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-8226.

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The presence of different types of defects is an important reality for manufacturers and users of engineering materials. Generally, the defects are either considered to be the unwanted products of impurities in the raw materials or to have been introduced during the manufacturing process. In high-quality steel materials, such as tool steel, the defects are usually non-metallic inclusions such as oxides or sulfides. Traditional methods for purity control during standard manufacturing practice are usually based on the light optical microscopy scanning of polished surfaces and some statistical ev
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Schmidt, Jacob Peter [Verfasser], Udo [Akademischer Betreuer] Kamps, and Erhard [Akademischer Betreuer] Cramer. "Generalized order statistics : stability and extreme value index estimation / Jacob Peter Schmidt ; Udo Kamps, Erhard Cramer." Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1156924413/34.

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Schmidt, Jacob [Verfasser], Udo [Akademischer Betreuer] Kamps, and Erhard [Akademischer Betreuer] Cramer. "Generalized order statistics : stability and extreme value index estimation / Jacob Peter Schmidt ; Udo Kamps, Erhard Cramer." Aachen : Universitätsbibliothek der RWTH Aachen, 2017. http://d-nb.info/1156924413/34.

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Engberg, Alexander. "An empirical comparison of extreme value modelling procedures for the estimation of high quantiles." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297063.

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The peaks over threshold (POT) method provides an attractive framework for estimating the risk of extreme events such as severe storms or large insurance claims. However, the conventional POT procedure, where the threshold excesses are modelled by a generalized Pareto distribution, suffers from small samples and subjective threshold selection. In recent years, two alternative approaches have been proposed in the form of mixture models that estimate the threshold and a folding procedure that generates larger tail samples. In this paper the empirical performances of the conventional POT procedur
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CHAVEZ, CHRISTIAM MIGUEL GONZALES. "VALUE AT RISK A COMPARISON OF METHODS TO CHOOSE THE SAMPLE FRACTION IN TAIL INDEX ESTIMATION OF GENERALIZED EXTREME VALUE DISTRIBUTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=2908@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Valor em Risco -VaR- já é parte das ferramentas habituais que um analista financeiro utiliza para estimar o risco de mercado. Na implementação do VaR é necessário que seja estimados quantis de baixa probabilidade para a distribuição condicional dos retornos dos portfólios. A metodologia tradicional para o cálculo do VaR requer a estimação de um modelo tipo GARCH com distribuição normal. Entretanto, a hipótese de normalidade condicional nem sempre é adequada, principalmente quando se deseja estimar o VaR em períodos atípicos
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Books on the topic "Generalized extreme value"

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Cheng, Russell. Embedded Distributions: Two Numerical Examples. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0007.

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This chapter illustrates use of (i) the score statistic and (ii) a goodness-of-fit statistic to test if an embedded model provides an adequate fit, in the latter case with critical values calculated by bootstrapping. Also illustrated is (iii) calculation of parameter confidence intervals and CDF confidence bands using both asymptotic theory and bootstrapping, and (iv) use of profile log-likelihood plots to display the form of the maximized log-likelihood and scatterplots for checking convergence to normality of estimated parameter distributions. Two different data sets are analysed. In the fir
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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Capstone: Application of NLTS to Real-World Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0010.

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This chapter investigates the use of Extreme Value Statistics (EVS) to probabilistically model extreme events separated as unstructured noise in signal processing. We apply a version of EVS that computes the likelihood of extreme discrepancies exceeding a selected threshold value within a given time interval. In theory, exceedances follow a Generalized Pareto (GP) distribution, and we run diagnostics to determine how well the data actually fit this distribution. If we find a reasonable fit, we can invert the GP distribution to solve for quantiles providing a useful noise diagnostic: return lev
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Spencer, Thomas. Number theory. Edited by Gernot Akemann, Jinho Baik, and Philippe Di Francesco. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198744191.013.24.

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This article examines some of the connections between random matrix theory (RMT) and number theory, including the modelling of the value distributions of the Riemann zeta function and other L-functions as well as the statistical distribution of their zeros. Number theory has been used in RMT to address seemingly disparate questions, such as modelling mean and extreme values of the Riemann zeta function and counting points on curves. One thing in common among the applications of RMT to number theory is the L-function. The statistics of the critical zeros of these functions are believed to be re
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Cheng, Russell. Examples of Embedded Distributions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0006.

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This chapter gives examples of probability distributions that, in their conventional parametrization, contain embedded models. Embeddedness is not intrinsic but depends on the parametrization. The simplest way to reveal and remove embeddedness is to reparametrize and make the log-likelihood, L, expandable as a Maclaurin series of one parameter, α‎: L = L0 + L1α‎ + L2α‎2 + … with L0 the log-likelihood of the embedded model hidden in the original parametrization. The quantity L1, rescaled using the information matrix, is the score statistic which can be used for formally comparing the original a
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Book chapters on the topic "Generalized extreme value"

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Singh, Vijay P. "Generalized Extreme Value Distribution." In Entropy-Based Parameter Estimation in Hydrology. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1431-0_11.

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Tsokos, Chris P. "Generalized Extreme Value Family of Probability Distributions." In International Encyclopedia of Statistical Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_427.

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Pickands, James. "Bayes Quantile Estimation and Threshold Selection for the Generalized Pareto Family." In Extreme Value Theory and Applications. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4613-3638-9_7.

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Mano, Shuhei, and Masaaki Sibuya. "Parameter Estimation of Generalized Beta Distributions and Its Application to a Historical Tsunami Magnitude Dataset." In Pioneering Works on Extreme Value Theory. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0768-4_1.

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Páez, Antonio, and Geneviève Boisjoly. "Non-proportional Substitution Patterns I: Generalized Extreme Value Models." In Use R! Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20719-8_7.

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Chankhachon, Sakon, and Sathit Intajag. "Generalized Extreme Value Filter to Remove Mixed Gaussian-Impulse Noise." In PRICAI 2016: Trends in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42911-3_5.

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Swetapadma, Sonali, and C. S. P. Ojha. "Plotting Positions for the Generalized Extreme Value Distribution: A Critique." In The Ganga River Basin: A Hydrometeorological Approach. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60869-9_13.

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Risser, Mark D., and Claudia Tebaldi. "Uncertainty and Extremes." In Uncertainty in Climate Change Research. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85542-9_21.

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Abstract The impactful nature of extreme weather events calls for a robust quantification of their statistics, if risks are to be reliably characterized for both current and future hazards. In this chapter, using examples from recent events, we describe the rationale and some applications of the main statistical tools for such quantification, Extreme Value Analysis, EVA. We present the basic modeling of block maxima through Generalized Extreme Value distributions and that of threshold exceedances, through the Generalized Pareto distribution, and point at some effective extensions of the standa
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Khosropour, Alireza, Hossein Aghababa, Ali Afzali-Kusha, and Behjat Forouzandeh. "Chip Level Statistical Leakage Power Estimation Using Generalized Extreme Value Distribution." In Integrated Circuit and System Design. Power and Timing Modeling, Optimization, and Simulation. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24154-3_18.

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Juan, Huang, and Luo Yinqi. "Anomaly Analysis of University Logistics Website Visits Based on Generalized Extreme Value Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4963-1_13.

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Conference papers on the topic "Generalized extreme value"

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Yinqi, Luo, Liu Liyin, Xie Guoyi, Luo Sui, and Li Suhang. "Analysis of Extreme Diurnal Temperature Range: Based on Generalized Extreme Value Model." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873678.

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Juan, Huang, and Liu Liyin. "Application of Generalized Extreme Value Model in the Analysis of Extreme High Temperature Weather." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873734.

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Jiaheng, Luo, Luo Yinqi, and Li Jianping. "Application of Generalized Extreme Value Model Based on Bayesian Parameter Estimation in Extreme High Temperature Weather Analysis." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873633.

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Yuniarti, Ike, Dedy Dwi Prastyo, and Setiawan. "Generalized Extreme Value Regression for Prediction of Meat Import Price Range in Indonesia." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875793.

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Wallace, Jon, Ramani Reddy, Dylan Pugh, and Jorge Pacheco. "Sour Service Pit Growth Predictions of Carbon Steel Using Extreme Value Statistics." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07657.

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Abstract The deepest pits formed during pitting corrosion of stainless steels have previously been shown to follow a Type III extreme value distribution, where a statistical upper bound on pit depth exists for a given point in time. In this study the Joint Generalized Extreme Value model parameterized with respect to both time and area is applied to predict pit growth of carbon steel under sour service conditions. Measurements of the most extreme pit depths were taken at different time durations to estimate model parameters and several statistical measures were used to assess the model goodnes
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Gang, Liu, and Luo Yinqi. "1960-2022 Extreme Low-Temperature Weather Analysis in Cengong County, Guizhou Province: Based on Generalized Extreme Value Model of Profile Likelihood Estimation." In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2024. https://doi.org/10.1109/iccwamtip64812.2024.10873656.

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Usmany, Felix, Sutikno, and Vita Ratnasari. "Parameter Estimation and Application of Spatial Extreme Value Modeling with Peaks over Threshold - Kumaraswamy Generalized Pareto (POT-KumGP) Method (Case Study: Extreme Rainfall in Maluku province)." In 2024 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). IEEE, 2024. https://doi.org/10.1109/agers65212.2024.10932971.

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Kasamatsu, Toru, Yoshiaki Okui, Masashi Kimura, Ryota Kano, Satoru Sakuma, and Hideo Tokida. "A study on live load factor considering actual traffic load for evaluation of existing bridges." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.0290.

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&lt;p&gt;Although B-live load is applied to bridge designs at present, considerable bridges had been designed with TL-20 in the previous design specifications. Most of these existing non-conformed bridges seem to be insufficient in load-carrying capacity under the current design code. In this study, a procedure for evaluating site-specific live load factors based on the actual traffic data for existing bridges is proposed. Two site-specific traffic data and three different girder bridges are employed to estimate live load factors for existing bridges as case study examples. The proposed method
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Oktaviarina, Affiati, Ayunin Sofro, and Hery Sutanto. "Generalized Extreme Value for Climate Change Analysis." In Proceedings of the International Conference on Science and Technology (ICST 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icst-18.2018.170.

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Hasan, Husna, Nadiah Mansor, and Nur Hanim Mohd Salleh. "Modeling extreme PM10 concentration in Malaysia using generalized extreme value distribution." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2014 (ICoMEIA 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4915666.

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Reports on the topic "Generalized extreme value"

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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the n
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Bäumler, Maximilian, and Matthias Lehmann. Generating representative test scenarios: The FUSE for Representativity (fuse4rep) process model for collecting and analysing traffic observation data. TU Dresden, 2024. http://dx.doi.org/10.26128/2024.2.

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Scenario-based testing is a pillar of assessing the effectiveness of automated driving systems (ADSs). For data-driven scenario-based testing, representative traffic scenarios need to describe real road traffic situations in compressed form and, as such, cover normal driving along with critical and accident situations originating from different data sources. Nevertheless, in the choice of data sources, a conflict often arises between sample quality and depth of information. Police accident data (PD) covering accident situations, for example, represent a full survey and thus have high sample qu
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