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Artículos de revistas sobre el tema "Generalized extreme value"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 26 de julio de 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Newman, Jeffrey P. "Normalization of network generalized extreme value models." Transportation Research Part B: Methodological 42, no. 10 (2008): 958–69. http://dx.doi.org/10.1016/j.trb.2008.04.001.

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12

Eriksson, Erik Anders. "Generalized extreme value discrete choice demand models." Regional Science and Urban Economics 16, no. 4 (1986): 547–72. http://dx.doi.org/10.1016/0166-0462(86)90023-2.

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13

Yang, Yu, HongGuang Sun, and Zheng Xu. "A mixture transmuted generalized extreme value distribution: Definition and properties." EPJ Web of Conferences 332 (2025): 01003. https://doi.org/10.1051/epjconf/202533201003.

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Extreme events are often described using generalized extreme value models, which are crucial for quantifying their impact. In prior studies, researchers have utilized the quadratic rank transmutation map to construct a comprehensive family of probability distributions, incorporating an additional parameter to significantly improve the flexibility of distribution modeling. To gain a deeper understanding of the statistical characteristics and patterns of extreme events, mixture distributions have been applied, such as the mixture normal distribution and the mixture of Gumbel distribution, among

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14

Cisse, Mamadou, Aliou Diop, Souleymane Bognini, and Nonvikan Karl-Augustt ALAHASSA. "Data Geometry and Extreme Value Distribution." Journal of Mathematics and Statistics Studies 2, no. 2 (2021): 06–15. http://dx.doi.org/10.32996/jmss.2021.2.2.2.

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In extreme values theory, there exist two approaches about data treatment: block maxima and peaks-over-threshold (POT) methods, which take in account data over a fixed value. But, those approaches are limited. We show that if a certain geometry is modeled with stochastic graphs, probabilities computed with Generalized Extreme Value (GEV) Distribution can be deflated. In other words, taking data geometry in account change extremes distribution. Otherwise, it appears that if the density characterizing the states space of data system is uniform, and if the quantile studied is positive, then the W

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15

Martins, Eduardo S., and Jery R. Stedinger. "Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data." Water Resources Research 36, no. 3 (2000): 737–44. http://dx.doi.org/10.1029/1999wr900330.

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16

Syed, Zishan Ali, Mohammad Mohammad Ahmed Almazah, Zahid Iqbal, and Ghulam Raza Khan. "The Risk Analysis and Modeling of Byco Petroleum in Pakistan Using Extreme Value Theory." Mathematical Problems in Engineering 2021 (September 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/2366469.

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The extreme value theory (EVT) has been used to model and measure the distribution of extreme minima of Byco Petroleum in the Pakistan stock market over the period from 2005 to 2012. This paper covers the investigation of distributions that are mostly used in finance including the generalized extreme value (GEV), generalized logistics (GL), and generalized Pareto (GPA) distribution. L-moment ratio diagram is being used to find the appropriate distributions among the distributions. L-moment diagram depicts that GEV and GL distributions are suitable to represent the extremes of Byco Petroleum Pa

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17

Rinaldi, Achi. "Sebaran Generalized Extreme Value (GEV) Dan Generalized Pareto (GP) untuk Pendugaan Curah Hujan Ekstrim di Wilayah DKI Jakarta." Al-Jabar : Jurnal Pendidikan Matematika 7, no. 1 (2016): 75–84. http://dx.doi.org/10.24042/ajpm.v7i1.137.

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Extreme event such as extreme rainfall have been analyzed and most concern for the country all around the world. There are two common distribution for extreme value which are Generalized Extreme Value distribution and Generalized Pareto distribution. These two distribution have shown good performace to estimate the parameter of extreme value. This research was aim to estimate parameter of extreme value using GEV distribution and GP distribution, and also to characterized effect of extreme event such as flood. The rainfall data was taken from BMKG for 5 location in DKI Jakarta. Both of distribu

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18

Ueki, Taro. "Generalized extreme value analysis of efficient evaluation of extreme values in random media criticality calculations." Progress in Nuclear Energy 173 (August 2024): 105236. http://dx.doi.org/10.1016/j.pnucene.2024.105236.

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19

El Adlouni, S., F. Chebana, and B. Bobée. "Generalized Extreme Value versus Halphen System: Exploratory Study." Journal of Hydrologic Engineering 15, no. 2 (2010): 79–89. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000152.

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20

ZENG, LANGCHE. "A Heteroscedastic Generalized Extreme Value Discrete Choice Model." Sociological Methods & Research 29, no. 1 (2000): 118–44. http://dx.doi.org/10.1177/0049124100029001006.

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21

Small, Kenneth A. "Approximate generalized extreme value models of discrete choice." Journal of Econometrics 62, no. 2 (1994): 351–82. http://dx.doi.org/10.1016/0304-4076(94)90028-0.

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22

Segers, Johan. "Generalized Pickands estimators for the extreme value index." Journal of Statistical Planning and Inference 128, no. 2 (2005): 381–96. http://dx.doi.org/10.1016/j.jspi.2003.11.004.

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23

Wei, Yonghua, and Gabriel Huerta. "Dynamic generalized extreme value modeling via particle filters." Communications in Statistics - Simulation and Computation 46, no. 8 (2016): 6324–41. http://dx.doi.org/10.1080/03610918.2016.1202275.

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24

Yee, Thomas W., and Alec G. Stephenson. "Vector generalized linear and additive extreme value models." Extremes 10, no. 1-2 (2007): 1–19. http://dx.doi.org/10.1007/s10687-007-0032-4.

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25

Wang, Xia, and Dipak K. Dey. "Generalized extreme value regression for ordinal response data." Environmental and Ecological Statistics 18, no. 4 (2010): 619–34. http://dx.doi.org/10.1007/s10651-010-0154-8.

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26

j, j., j. j, and j. j. "Vector Generalized Additive Models for Extreme Rainfall Data Analysis: A case study in South Korea." Korean Data Analysis Society 25, no. 5 (2023): 1595–607. http://dx.doi.org/10.37727/jkdas.2023.25.5.1595.

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Extreme value theory (EVT) is the branch of statistics concerned with inference about the tails of the distribution function. Accurate estimation of the year return levels of climate extremes is a critical step in the projection of future climate and in engineering design for disaster response. The study of non-stationary behavior in the extremes is important to analyze data in environmental sciences, climate, finance, or sports. The largest order statistics is an extension of the block maxima approach that is used in extreme value modeling. However, in Korea, the scientific literature usually

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27

Ginting, Segel, and William M. Putuhena. "Hujan rancangan berdasarkan analisis frekuensi regional dengan metode tl-moment." JURNAL SUMBER DAYA AIR 12, no. 1 (2017): 1–16. http://dx.doi.org/10.32679/jsda.v12i1.160.

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The designstorm wereestimated by applying the regional frequency analysis provides benefits to a datasetwith limited amount of data has many advantages. Minimum data used in calculating the amount of design stromhas a very large error for higherreturn period. Therefore, the regional frequency analysis was used based on TL-moments method. There arethree types of probability distributions used in this study, namely the Generalized Extreme Value (GEV), Generalized Pareto (GPA) and the Generalized Logistic (GLO). Two of the three typesprobability distributions are the best choice by the TL-moment

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28

Sung, Yong Kyu, and Joong K. Sohn. "Erratum to "Prediction of extreme rainfall with a generalized extreme value distribution"." Journal of the Korean Data and Information Science Society 24, no. 5 (2013): 1101. http://dx.doi.org/10.7465/jkdi.2013.24.5.1101.

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29

FENG, Song, Saralees NADARAJAH, and Qi HU. "Modeling Annual Extreme Precipitation in China Using the Generalized Extreme Value Distribution." Journal of the Meteorological Society of Japan 85, no. 5 (2007): 599–613. http://dx.doi.org/10.2151/jmsj.85.599.

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30

Henriques-Rodrigues, Lígia, and M. Ivette Gomes. "Box-Cox Transformations and Bias Reduction in Extreme Value Theory." Computational and Mathematical Methods 2022 (March 10, 2022): 1–15. http://dx.doi.org/10.1155/2022/3854763.

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The Box-Cox transformations are used to make the data more suitable for statistical analysis. We know from the literature that this transformation of the data can increase the rate of convergence of the tail of the distribution to the generalized extreme value distribution, and as a byproduct, the bias of the estimation procedure is reduced. The reduction of bias of the Hill estimator has been widely addressed in the literature of extreme value theory. Several techniques have been used to achieve such reduction of bias, either by removing the main component of the bias of the Hill estimator of

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31

Carollo, C., I. Astin, and J. Graff. "Vertical structure of extreme currents in the Faroe-Bank Channel." Annales Geophysicae 23, no. 6 (2005): 1977–86. http://dx.doi.org/10.5194/angeo-23-1977-2005.

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Abstract. Extreme currents are studied with the aim of understanding their vertical and spatial structures in the Faroe-Bank Channel. Acoustic Doppler Current Profiler time series recorded in 3 deployments in this channel were investigated. To understand the main features of extreme events, the measurements were separated into their components through filtering and tidal analysis before applying the extreme value theory to the surge component. The Generalized Extreme Value (GEV) distribution and the Generalized Pareto Distribution (GPD) were used to study the variation of surge extremes from n

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32

Rehman, Adil, and Huai Zhang. "Generalized Extreme Value Distribution for Modeling Earthquake Risk in Makran Subduction Zone Using Extreme Value Theory." Indonesian Journal of Earth Sciences 3, no. 2 (2023): A819. http://dx.doi.org/10.52562/injoes.2023.819.

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The long-term pattern of severe incidents is one of the most crucial and fascinating topics of seismic events. This work aims to analyze the maximum annual earthquake magnitude in the Makran subduction zone using extreme value theory by implementing the block maxima method. The seismic data utilized for the current study was collected from the International Seismological Center (ISC) ranging from 1934 to 2022. The extreme parameters have fitted utilizing the generalized extreme value distribution. Numerous plots of the generalized extreme value distribution approach gave the accuracy of the us

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33

Nakajima, Jouchi, Tsuyoshi Kunihama, and Yasuhiro Omori. "Bayesian modeling of dynamic extreme values: extension of generalized extreme value distributions with latent stochastic processes." Journal of Applied Statistics 44, no. 7 (2016): 1248–68. http://dx.doi.org/10.1080/02664763.2016.1201796.

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34

Balakrishnan, N., P. S. Chan, and M. Ahsanullah. "Recurrence relations for moments of record values from generalized extreme value distribution." Communications in Statistics - Theory and Methods 22, no. 5 (1993): 1471–82. http://dx.doi.org/10.1080/03610929308831097.

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35

Ahsanullah, M. "Some inferences of the generalized extreme value distribution based on record values." Journal of Mathematical Sciences 78, no. 1 (1996): 2–10. http://dx.doi.org/10.1007/bf02367949.

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36

Huang, Whitney K., Michael L. Stein, David J. McInerney, Shanshan Sun, and Elisabeth J. Moyer. "Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 1 (2016): 79–103. http://dx.doi.org/10.5194/ascmo-2-79-2016.

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Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO2) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extrem

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37

Tanprayoon, Ekapak, Unchalee Tonggumnead, and Sirinapa Aryuyuen. "A New Extension of Generalized Extreme Value Distribution: Extreme Value Analysis and Return Level Estimation of the Rainfall Data." Trends in Sciences 20, no. 1 (2022): 4034. http://dx.doi.org/10.48048/tis.2023.4034.

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This paper presents an extension of the generalized extreme value (GEV) distribution, based on the T-X family of distributions: Gompertz-generated family of distributions that make the existing distribution more flexible called the Gompertz-general extreme value (Go-GEV) distribution. Some properties of the proposed distribution are introduced, and a new distribution is applied to actual data, namely rainfall in Lopburi Province, by comparing the proposed model with the traditional GEV distribution and estimating the return levels of the rainfall in Lopburi Province. Results showed that the Go

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38

H., A., Mohamed Abouhawwash, and H. S. "New Generalized Extreme Value Distribution and its Bivariate Extension." International Journal of Computer Applications 173, no. 3 (2017): 1–10. http://dx.doi.org/10.5120/ijca2017915276.

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39

SANDOVAL, CARLOS ESCALANTE, and JOSE RAYNAL-VILLASEÑOR. "Trivariate generalized extreme value distribution in flood frequency analysis." Hydrological Sciences Journal 53, no. 3 (2008): 550–67. http://dx.doi.org/10.1623/hysj.53.3.550.

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40

Scarf, P. A. "Estimation for a four parameter generalized extreme value distribution." Communications in Statistics - Theory and Methods 21, no. 8 (1992): 2185–201. http://dx.doi.org/10.1080/03610929208830906.

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41

Gomes, M. Ivette, M. João Martins, and M. Manuela Neves. "Generalized Jackknife-Based Estimators for Univariate Extreme-Value Modeling." Communications in Statistics - Theory and Methods 42, no. 7 (2013): 1227–45. http://dx.doi.org/10.1080/03610926.2012.725263.

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42

Choi, Ki-Hong, and Choon-Geol Moon. "Generalized extreme value model and additively separable generator function." Journal of Econometrics 76, no. 1-2 (1997): 129–40. http://dx.doi.org/10.1016/0304-4076(95)01785-2.

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43

González, Eduardo Gutiérrez, Miguel Cerón Villegas, Sandra Ibeth Ochoa García, Luis Alberto Heredia Rojas, and Alberto Cejudo García. "A PARAMETRIC BOOTSTRAP TEST FOR GENERALIZED EXTREME VALUE DISTRIBUTION." Far East Journal of Mathematical Sciences (FJMS) 96, no. 6 (2015): 693–708. http://dx.doi.org/10.17654/fjmsmar2015_693_708.

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44

Nidhin, K., and C. Chandran. "Importance of Generalized Logistic Distribution in Extreme Value Modeling." Applied Mathematics 04, no. 03 (2013): 560–73. http://dx.doi.org/10.4236/am.2013.43080.

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45

Bertin, Eric, and Maxime Clusel. "Generalized extreme value statistics and sum of correlated variables." Journal of Physics A: Mathematical and General 39, no. 24 (2006): 7607–19. http://dx.doi.org/10.1088/0305-4470/39/24/001.

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46

BALI, TURAN G. "A Generalized Extreme Value Approach to Financial Risk Measurement." Journal of Money, Credit and Banking 39, no. 7 (2007): 1613–49. http://dx.doi.org/10.1111/j.1538-4616.2007.00081.x.

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47

Cai, Yuzhi, and Dominic Hames. "Minimum Sample Size Determination for Generalized Extreme Value Distribution." Communications in Statistics - Simulation and Computation 40, no. 1 (2010): 87–98. http://dx.doi.org/10.1080/03610918.2010.530368.

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48

Abu El-Magd, Noura A. T. "TL-moments of the exponentiated generalized extreme value distribution." Journal of Advanced Research 1, no. 4 (2010): 351–59. http://dx.doi.org/10.1016/j.jare.2010.06.003.

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49

Muja, Aranit. "Extreme Value of Intraday Returns." Academic Journal of Interdisciplinary Studies 7, no. 3 (2018): 187–94. http://dx.doi.org/10.2478/ajis-2018-0069.

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Abstract The aim of this research paper is to study the properties of intraday returns, in a time range from one to fifteen minutes. In order to perform this analysis, we consider four sets of historical intraday returns for FTSE-MIB index. The first series consist of intraday returns with one-minute frequency, represented in log scale, which includes the period from 01.04.2011 till 30.09.2011. The consideration period for the other series does not vary, but the frequencies which we calculate the returns with, do. In detail, we took in consideration returns generated in 1, 5, 10 and 15 minutes

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50

Laux, Patrick, Elena Weber, David Feldmann, and Harald Kunstmann. "The Robustness of the Derived Design Life Levels of Heavy Precipitation Events in the Pre-Alpine Oberland Region of Southern Germany." Atmosphere 14, no. 9 (2023): 1384. http://dx.doi.org/10.3390/atmos14091384.

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Extreme value analysis (EVA) is well-established to derive hydrometeorological design values for infrastructures that have to withstand extreme events. Since there is concern about increased extremes with higher hazard potential under climate change, alterations of EVA are introduced for which statistical properties are no longer assumed to be constant but vary over time. In this study, both stationary and non-stationary EVA models are used to derive design life levels (DLLs) of daily precipitation in the pre-alpine Oberland region of Southern Germany, an orographically complex region characte

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