An Introduction to Statistical Modeling of Extreme Values
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DI-GNN represents a significant advancement in modeling rare and extreme events, transcending the limitations of traditional feature engineering by embedding EVT principles directly into the learning process.
“The Distribution-Informed Graph Neural Network (DI-GNN) represents a significant advancement in the modeling of rare and extreme events, transcending the limitations of traditional feature engineering by embedding Extreme Value Theory (EVT) principles directly into the learning process.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
Unlike heuristic methods, DI-GNN's EVT-based approach provides a principled and robust framework for forecasting extreme climate events.
“Unlike heuristic methods, DI-GNN's EVT-based approach provides a principled and robust framework for forecasting extreme climate events [29, 28].”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
EVT-based approaches allow for more robust identification of extreme events by modeling the tails of temperature distributions, addressing the inadequacies of fixed percentile thresholds.
“EVT-based approaches allow for more robust identification of extreme events by modeling the tails of temperature distributions, addressing the inadequacies of fixed percentile thresholds.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
DI-GNN incorporates GPD-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences.
“DI-GNN incorporates Generalized Pareto Distribution (GPD)-derived descriptors into the feature space, adjacency matrix, and loss function to enhance its sensitivity to rare heatwave occurrences.”
From Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs (2024) · cites this paper as [29]
Cites
A Point Process Characterization of Extremes
Coles, S. (2001) · Springer series in statistics · doi:10.1007/978-1-4471-3675-0_7 · Cited by 1 · see citing works
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Changes in Climate Extremes and their Impacts on the Natural Physical Environment
Seneviratne, S. I., Nicholls, N., Easterling, D. R., Goodess, C. M., Kanae, S., Kossin, J. P., Luo, Y., Marengo, J. A., McInnes, K. L., Rahimi, M., et al. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139177245.006 · Cited by 2,559 · see citing works
Top Incomes in the Long Run of History
Atkinson, A. B., Piketty, T., & Saez, E. (2011) · Journal of Economic Literature · doi:10.1257/jel.49.1.3 · Cited by 2,362 · see citing works
Climate extremes and the carbon cycle
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M. D., Seneviratne, S. I., Jakob Zscheischler, A., Beer, C., Buchmann, N., Frank, D., et al. (2013) · Nature · doi:10.1038/nature12350 · Cited by 2,025 · see citing works
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M. J., et al. (2010) · Reviews of Geophysics · doi:10.1029/2009rg000314 · Cited by 1,836 · see citing works
Human contribution to the European heatwave of 2003
Stott, P. A., Stone, D. A., & Allen, M. R. (2004) · Nature · doi:10.1038/nature03089 · Cited by 1,735 · see citing works
Statistics of extremes in hydrology
Katz, R. W., Parlange, M. B., & Naveau, P. (2002) · Advances in Water Resources · doi:10.1016/s0309-1708(02)00056-8 · Cited by 1,721 · see citing works
Cannon, A. J., Sobie, S. R., & Murdock, T. Q. (2015) · Journal of Climate · doi:10.1175/jcli-d-14-00754.1 · Cited by 1,700 · see citing works
The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., & García-Herrera, R. (2011) · Science · doi:10.1126/science.1201224 · Cited by 1,656 · see citing works
Westra, S., Fowler, H. J., Evans, J. P., Alexander, L. V., Berg, P., Johnson, F., Kendon, E. J., Lenderink, G., & Roberts, N. M. (2014) · Reviews of Geophysics · doi:10.1002/2014rg000464 · Cited by 1,533 · see citing works
Future extreme events in European climate: an exploration of regional climate model projections
Beniston, M., Stephenson, D., Christensen, O., Ferro, C., Frei, C., Goyette, S., Halsnæs, K., Holt, T., Jylhä, K., Koffi, B., et al. (2007) · Climatic Change · doi:10.1007/s10584-006-9226-z · Cited by 1,487 · see citing works
Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring
Westerling, A. L. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0178 · Cited by 1,322 · see citing works
Paul Embrechts, A. (2011) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-642-04898-2_466 · Cited by 985 · see citing works
Doubling of coastal flooding frequency within decades due to sea-level rise
Vitousek, S., Barnard, P. L., Fletcher, C. H., Frazer, L. N., Erikson, L., & Storlazzi, C. D. (2017) · Scientific Reports · doi:10.1038/s41598-017-01362-7 · Cited by 925 · see citing works
A compound event framework for understanding extreme impacts
Leonard, M., Westra, S., Phatak, A., Lambert, M. F., van den Hurk, B., McInnes, K. L., Risbey, J. S., Schuster, S. S., Jakob, D., & Stafford‐Smith, M. (2013) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.252 · Cited by 884 · see citing works
Global Changes in Drought Conditions Under Different Levels of Warming
Naumann, G., Alfieri, L., Wyser, K., Mentaschi, L., Betts, R., Carrão, H., Spinoni, J., Vogt, J., & Feyen, L. (2018) · Geophysical Research Letters · doi:10.1002/2017gl076521 · Cited by 839 · see citing works
Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
Idea Generation and the Quality of the Best Idea
Girotra, K., Terwiesch, C., & Ulrich, K. T. (2010) · Management Science · doi:10.1287/mnsc.1090.1144 · Cited by 755 · see citing works
Bias Correction, Quantile Mapping, and Downscaling: Revisiting the Inflation Issue
Maraun, D. (2013) · Journal of Climate · doi:10.1175/jcli-d-12-00821.1 · Cited by 762 · see citing works
Oliver, E. C. J., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J., Holbrook, N. J., Schlegel, R. W., & Gupta, A. S. (2020) · Annual Review of Marine Science · doi:10.1146/annurev-marine-032720-095144 · Cited by 772 · see citing works
Frei, C., Schöll, R., Fukutome, S., Schmidli, J., & Vidale, P. L. (2006) · Journal of Geophysical Research Atmospheres · doi:10.1029/2005jd005965 · Cited by 679 · see citing works
Physically based assessment of hurricane surge threat under climate change
Lin, N., Emanuel, K., Oppenheimer, M., & Vanmarcke, E. H. (2012) · Nature Climate Change · doi:10.1038/nclimate1389 · Cited by 663 · see citing works
Beretta, S., & Romano, S. (2016) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2016.06.020 · Cited by 635 · see citing works
Continued warming could transform Greater Yellowstone fire regimes by mid-21st century
Westerling, A. L., Turner, M. G., Smithwick, E. A. H., Romme, W. H., & Ryan, M. G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110199108 · Cited by 630 · see citing works
Kirezci, E., Young, I. R., Ranasinghe, R., Muis, S., Nicholls, R. J., Lincke, D., & Hinkel, J. (2020) · Scientific Reports · doi:10.1038/s41598-020-67736-6 · Cited by 630 · see citing works
Quantifying the evolution of individual scientific impact
Sinatra, R., Wang, D., Deville, P., Song, C., & Barabási, A.-Ĺ. (2016) · Science · doi:10.1126/science.aaf5239 · Cited by 613 · see citing works
extRemes 2.0: An Extreme Value Analysis Package in R
Gilleland, E., & Katz, R. W. (2016) · Journal of Statistical Software · doi:10.18637/jss.v072.i08 · Cited by 614 · see citing works
Statistical Modeling of Spatial Extremes
Davison, A. C., Padoan, S. A., & Ribatet, M. (2012) · Statistical Science · doi:10.1214/11-sts376 · Cited by 585 · see citing works
Intensity and frequency of extreme novel epidemics
Marani, M., Katul, G. G., Pan, W., & Parolari, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105482118 · Cited by 587 · see citing works
Revisiting the Concepts of Return Period and Risk for Nonstationary Hydrologic Extreme Events
Salas, J. D., & Obeysekera, J. T. B. (2013) · Journal of Hydrologic Engineering · doi:10.1061/(asce)he.1943-5584.0000820 · Cited by 549 · see citing works
Estimating Extremes in Transient Climate Change Simulations
Kharin, V., & Zwiers, F. W. (2005) · Journal of Climate · doi:10.1175/jcli3320.1 · Cited by 543 · see citing works
Non-stationary extreme value analysis in a changing climate
Cheng, L., AghaKouchak, A., Gilleland, E., & Katz, R. W. (2014) · Climatic Change · doi:10.1007/s10584-014-1254-5 · Cited by 537 · see citing works
Monitoring and Understanding Trends in Extreme Storms: State of Knowledge
Kunkel, K. E., Karl, T. R., Brooks, H., Kossin, J., Lawrimore, J. H., Arndt, D., Bosart, L., Changnon, D., Cutter, S. L., Doesken, N., et al. (2013) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00262.1 · Cited by 536 · see citing works
Bayesian Spatial Modeling of Extreme Precipitation Return Levels
Cooley, D., Nychka, D. W., & Naveau, P. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000000780 · Cited by 525 · see citing works
Statistical Analysis of Extreme Values
Reiss, R., & Thomas, M. E. (2007) · Birkhäuser Basel eBooks · doi:10.1007/978-3-7643-7399-3 · Cited by 524 · see citing works
Cheng, L., & AghaKouchak, A. (2014) · Scientific Reports · doi:10.1038/srep07093 · Cited by 520 · see citing works
Characterizing Adversarial Subspaces Using Local Intrinsic DimensionalityPreprint
Ma, X., Li, B., Wang, Y., Erfani, S., Wijewickrema, S. N. R., Schoenebeck, G. R., Song, D. X., Houle, M. E., & Bailey, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.02613 · Cited by 508 · see citing works
Castaldi, C., Frenken, K., & Los, B. (2014) · Regional Studies · doi:10.1080/00343404.2014.940305 · Cited by 497 · see citing works
Changes in extreme high water levels based on a quasi‐global tide‐gauge data set
Menéndez, M., & Woodworth, P. L. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jc005997 · Cited by 498 · see citing works
Explaining Extreme Events of 2011 from a Climate Perspective
Peterson, T. C., Stott, P. A., & Herring, S. C. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-12-00021.1 · Cited by 482 · see citing works
Stationarity is undead: Uncertainty dominates the distribution of extremes
Serinaldi, F., & Kilsby, C. G. (2015) · Advances in Water Resources · doi:10.1016/j.advwatres.2014.12.013 · Cited by 481 · see citing works
Modelling sea level rise impacts on storm surges along US coasts
Tebaldi, C., Strauss, B., & Zervas, C. E. (2012) · Environmental Research Letters · doi:10.1088/1748-9326/7/1/014032 · Cited by 481 · see citing works
Attribution of the Australian bushfire risk to anthropogenic climate change
van Oldenborgh, G. J., Krikken, F., Lewis, S. C., Leach, N. J., Lehner, F., Saunders, K., van Weele, M., Haustein, K., Li, S., Wallom, D. C. H., et al. (2021) · Natural hazards and earth system sciences · doi:10.5194/nhess-21-941-2021 · Cited by 468 · see citing works
Peterson, T. C., Heim, R. R., Hirsch, R. M., Kaiser, D. P., Brooks, H. E., Diffenbaugh, N. S., Dole, R. M., Giovannettone, J., Guirguis, K. J., Karl, T. R., et al. (2013) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-12-00066.1 · Cited by 467 · see citing works
Khaliq, M. N., Ouarda, T. B. M. J., Ondo, J.-C., Gachon, P., & Bobée, B. (2006) · Journal of Hydrology · doi:10.1016/j.jhydrol.2006.03.004 · Cited by 459 · see citing works
Zhou, Q., Leng, G., Su, J., & Ren, Y. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.12.184 · Cited by 459 · see citing works
Alexander, L. V. (2015) · Weather and Climate Extremes · doi:10.1016/j.wace.2015.10.007 · Cited by 459 · see citing works
Flood frequency analysis for nonstationary annual peak records in an urban drainage basin
Villarini, G., Smith, J. A., Serinaldi, F., Bales, J. D., Bates, P., & Krajewski, W. F. (2009) · Advances in Water Resources · doi:10.1016/j.advwatres.2009.05.003 · Cited by 454 · see citing works
Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., & Slangen, A. B. A. (2017) · Nature Communications · doi:10.1038/ncomms16075 · Cited by 436 · see citing works
Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing
Liu, C.-F., Bennis, M., Debbah, M., & Poor, H. V. (2019) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2898573 · Cited by 435 · see citing works
Generalized maximum likelihood estimators for the nonstationary generalized extreme value model
Adlouni, S. E., Ouarda, T. B. M. J., Zhang, X., Roy, R., & Bobée, B. (2007) · Water Resources Research · doi:10.1029/2005wr004545 · Cited by 428 · see citing works
Shongwe, M., van Oldenborgh, G. J., van den Hurk, B., de Boer, B., Coelho, C. A. S., & van Aalst, M. (2009) · Journal of Climate · doi:10.1175/2009jcli2317.1 · Cited by 429 · see citing works
Stéphane Hallegatte, A., Ranger, N. A., Mestre, O., Dumas, P., Corfee-Morlot, J., Herweijer, C., & Wood, R. M. (2010) · Climatic Change · doi:10.1007/s10584-010-9978-3 · Cited by 427 · see citing works
An Application of Extreme Value Theory for Measuring Financial Risk
Gilli, M., & Këllezi, E. (2006) · Computational Economics · doi:10.1007/s10614-006-9025-7 · Cited by 425 · see citing works
The physics of climate variability and climate change
Ghil, M., & Lucarini, V. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.035002 · Cited by 413 · see citing works
Asymmetric copula in multivariate flood frequency analysis
Grimaldi, S., & Serinaldi, F. (2005) · Advances in Water Resources · doi:10.1016/j.advwatres.2005.09.005 · Cited by 413 · see citing works
Marsooli, R., Lin, N., Emanuel, K., & Feng, K. (2019) · Nature Communications · doi:10.1038/s41467-019-11755-z · Cited by 413 · see citing works
Human water consumption intensifies hydrological drought worldwide
Wada, Y., van Beek, R., Wanders, N., & Bierkens, M. F. P. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/3/034036 · Cited by 400 · see citing works
The extreme value theory approach to safety estimation
Songchitruksa, P., & Tarko, A. P. (2006) · Accident Analysis & Prevention · doi:10.1016/j.aap.2006.02.003 · Cited by 396 · see citing works
Vautard, R., Gobiet, A., Jacob, D., Belda, M., Colette, A., Déqué, M., Fernández, J., García‐Díez, M., Goergen, K., Güttler, I., et al. (2013) · Climate Dynamics · doi:10.1007/s00382-013-1714-z · Cited by 404 · see citing works
Guidelines for determining flood flow frequency — Bulletin 17C
England, J. F., Cohn, T. A., Faber, B. A., Stedinger, J. R., Thomas, W. O., Veilleux, A. G., Kiang, J. E., & Mason, R. R. (2018) · Techniques and methods · doi:10.3133/tm4b5 · Cited by 391 · see citing works
The nexus between atmospheric rivers and extreme precipitation across Europe
Lavers, D. A., & Villarini, G. (2013) · Geophysical Research Letters · doi:10.1002/grl.50636 · Cited by 388 · see citing works
The European climate under a 2 °C global warming
Vautard, R., Gobiet, A., Sobolowski, S., Kjellström, E., Stegehuis, A. I., Watkiss, P., Mendlik, T., Landgren, O., Nikulin, G., Teichmann, C., et al. (2014) · Environmental Research Letters · doi:10.1088/1748-9326/9/3/034006 · Cited by 394 · see citing works
Risser, M. D., & Wehner, M. F. (2017) · Geophysical Research Letters · doi:10.1002/2017gl075888 · Cited by 397 · see citing works
A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions
Nazmi, N., Rahman, M. A. A., Yamamoto, S.-I., Ahmad, S. A., Zamzuri, H., & Mazlan, S. A. (2016) · Sensors · doi:10.3390/s16081304 · Cited by 379 · see citing works
Bevacqua, E., Maraun, D., Haff, I. H., Widmann, M., & Vrac, M. (2017) · Hydrology and earth system sciences · doi:10.5194/hess-21-2701-2017 · Cited by 375 · see citing works
Multi-class Open Set Recognition Using Probability of Inclusion
Jain, L. P., Scheirer, W. J., & Boult, T. E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10578-9_26 · Cited by 362 · see citing works
Lavers, D. A., Villarini, G., Allan, R. P., Wood, E. F., & Wade, A. J. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2012jd018027 · Cited by 365 · see citing works
A global evaluation of streamflow drought characteristics
Fleig, A. K., Tallaksen, L. M., Hisdal, H., & Demuth, S. (2006) · Hydrology and earth system sciences · doi:10.5194/hess-10-535-2006 · Cited by 359 · see citing works
Slater, L. J., Anderson, B. J., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T. K. R., et al. (2021) · Hydrology and earth system sciences · doi:10.5194/hess-25-3897-2021 · Cited by 379 · see citing works
Khaliq, M. N., Ouarda, T. B. M. J., Gachon, P., Sushama, L., & St‐Hilaire, A. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.01.035 · Cited by 357 · see citing works
A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation
Blum, M. G. B., Nunes, M. A., Prangle, D., & Sisson, S. A. (2013) · Statistical Science · doi:10.1214/12-sts406 · Cited by 355 · see citing works
Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes
Ghosh, S., Das, D., Kao, S., & Ganguly, A. R. (2011) · Nature Climate Change · doi:10.1038/nclimate1327 · Cited by 354 · see citing works
Nikulin, G., Kjellström, E., Hansson, U., Strandberg, G., & Ullerstig, A. (2010) · Tellus A Dynamic Meteorology and Oceanography · doi:10.1111/j.1600-0870.2010.00466.x · Cited by 354 · see citing works
Use of a Gaussian copula for multivariate extreme value analysis: Some case studies in hydrology
Renard, B., & Lang, M. (2006) · Advances in Water Resources · doi:10.1016/j.advwatres.2006.08.001 · Cited by 347 · see citing works
WAFO - A Matlab Toolbox For Analysis of Random Waves And Loads
Brodtkorb, P. A., Johannesson, P., Lindgren, G., Rychlik, I., Rydén, J., & Sjö, E. (2000) · The Proceedings of the ... International Offshore and Polar Engineering Conference · Cited by 347 · see citing works
Sea-level rise exponentially increases coastal flood frequency
Taherkhani, M., Vitousek, S., Barnard, P. L., Frazer, L. N., Anderson, T. R., & Fletcher, C. H. (2020) · Scientific Reports · doi:10.1038/s41598-020-62188-4 · Cited by 347 · see citing works
Flood peak distributions for the eastern United States
Villarini, G., & Smith, J. A. (2010) · Water Resources Research · doi:10.1029/2009wr008395 · Cited by 343 · see citing works
Projections of extreme storm surge levels along Europe
Vousdoukas, M. I., Voukouvalas, E. G., Annunziato, A., Giardino, A., & Feyen, L. (2016) · Climate Dynamics · doi:10.1007/s00382-016-3019-5 · Cited by 338 · see citing works
Rudd, E. M., Jain, L. P., Scheirer, W. J., & Boult, T. E. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2707495 · Cited by 338 · see citing works
Value-at-risk versus expected shortfall: A practical perspective
Yamai, Y., & Yoshiba, T. (2004) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2004.08.010 · Cited by 335 · see citing works
Measuring compound flood potential from river discharge and storm surge extremes at the global scale
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T., Haigh, I. D., Wahl, T., Winsemius, H., & Ward, P. J. (2020) · Natural hazards and earth system sciences · doi:10.5194/nhess-20-489-2020 · Cited by 343 · see citing works
Probabilistic Forecasting and Bayesian Data Assimilation
Reich, S., & Cotter, C. J. (2015) · Cambridge University Press eBooks · doi:10.1017/cbo9781107706804 · Cited by 332 · see citing works
Zheng, L., Sayed, T., & Mannering, F. (2020) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2020.100142 · Cited by 334 · see citing works
Storminess over the North Atlantic and northwestern Europe—A review
Feser, F., Barcikowska, M. J., Krueger, O., Schenk, F., Weiße, R., & Xia, L. (2014) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.2364 · Cited by 333 · see citing works
Grotjahn, R., Black, R. X., Leung, L.-Y. R., Wehner, M. F., Barlow, M., Bosilovich, M. G., Gershunov, A., Gutowski, W. J., Gyakum, J. R., Katz, R. W., et al. (2015) · Climate Dynamics · doi:10.1007/s00382-015-2638-6 · Cited by 341 · see citing works
Recent trends in the regime of extreme rainfall in the Central Sahel
Panthou, G., Vischel, T., & Lebel, T. (2014) · International Journal of Climatology · doi:10.1002/joc.3984 · Cited by 331 · see citing works
Dankers, R., & Feyen, L. (2008) · Journal of Geophysical Research Atmospheres · doi:10.1029/2007jd009719 · Cited by 329 · see citing works
Risk assessment of hurricane storm surge for New York City
Lin, N., Emanuel, K., Smith, J. A., & Vanmarcke, E. H. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jd013630 · Cited by 329 · see citing works
A protocol for probabilistic extreme event attribution analyses
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., et al. (2020) · Advances in Statistical Climatology, Meteorology and Oceanography · doi:10.5194/ascmo-6-177-2020 · Cited by 328 · see citing works
Shongwe, M. E., van Oldenborgh, G. J., van den Hurk, B., & van Aalst, M. (2011) · Journal of Climate · doi:10.1175/2010jcli2883.1 · Cited by 313 · see citing works
Generalized Additive Modelling of Sample Extremes
Chavez‐Demoulin, V., & Davison, A. C. (2004) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2005.00479.x · Cited by 313 · see citing works
Extreme sea levels at different global warming levels
Tebaldi, C., Ranasinghe, R., Vousdoukas, M. I., Rasmussen, D. J., Vega‐Westhoff, B., Kirezci, E., Kopp, R. E., Sriver, R. L., & Mentaschi, L. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01127-1 · Cited by 310 · see citing works
Trends and extremes of drought indices throughout the 20th century in the Mediterranean
Sousa, P. M., Trigo, R. M., Aizpurua, P., Nieto, R., Gimeno, L., & García‐Herrera, R. (2011) · Natural hazards and earth system sciences · doi:10.5194/nhess-11-33-2011 · Cited by 309 · see citing works
Dankers, R., Arnell, N. W., Clark, D. B., Falloon, P., Fekete, B. M., Gosling, S. N., Heinke, J., Kim, H., Masaki, Y., Satoh, Y., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1302078110 · Cited by 308 · see citing works
Personal Authentication Using Hand Vein Triangulation and Knuckle Shape
Kumar, A., & Prathyusha, K. V. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2023153 · Cited by 307 · see citing works
Global changes in extreme daily temperature since 1950
Brown, S. J., Caesar, J., & Ferro, C. A. T. (2008) · Journal of Geophysical Research Atmospheres · doi:10.1029/2006jd008091 · Cited by 306 · see citing works
Forecast verification: Current status and future directions
Casati, B., Wilson, L. J., Stephenson, D. B., Nurmi, P., Ghelli, A., Pocernich, M., Damrath, U., Ebert, E. E., Brown, B. G., & Mason, S. (2008) · Meteorological Applications · doi:10.1002/met.52 · Cited by 306 · see citing works
Schölzel, C., & Friederichs, P. (2008) · Nonlinear processes in geophysics · doi:10.5194/npg-15-761-2008 · Cited by 303 · see citing works
Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review
Venäläinen, A. K., Lehtonen, I., Laapas, M., Ruosteenoja, K., Tikkanen, O., Viiri, H., Ikonen, V., & Peltola, H. M. (2020) · Global Change Biology · doi:10.1111/gcb.15183 · Cited by 297 · see citing works
A fully probabilistic approach to extreme rainfall modeling
Coles, S., Pericchi, L. R., & Sisson, S. A. (2003) · Journal of Hydrology · doi:10.1016/s0022-1694(02)00353-0 · Cited by 291 · see citing works
Self-citation
- Authors
- Coles, S.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Coles, Stuart
- Venue
- Springer Series in Statistics
- Publisher
- Springer London
- Published
- 2001
- ISSN
- 0172-7397 (ISSN-L)
- 2197-568X
- OpenAlex
- W1500657154
- Cited by
- 7,352
- References
- 1
- Citations by year
- 2026: 459 · 2025: 466 · 2024: 393 · 2023: 457 · 2022: 397 · 2021: 483 · 2020: 429 · 2019: 417 · 2018: 398 · 2017: 381 · 2016: 389 · 2015: 371 · 2014: 366 · 2013: 324 · 2012: 314
- FWCI
- 27.09
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer series in statistics (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Probability and Risk Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- extreme value theory, extreme value distribution, tail behavior of distributions
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