The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An Introduction to Statistical Modeling of Extreme Values
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
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
Self-citation
Cited by
Sorted by citations
Items 201–300 of 1,004
Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection
Naveau, P., Huser, R., Ribereau, P., & Hannart, A. (2016) · Water Resources Research · doi:10.1002/2015wr018552 · Cited by 188
Storm-induced marine flooding: Lessons from a multidisciplinary approach
Chaumillon, É., Bertin, X., Fortunato, A. B., Bajo, M., Schneider, J.-L., Dézileau, L., Walsh, J. P., Michelot, A., Chauveau, É., Créach, A., et al. (2016) · Earth-Science Reviews · doi:10.1016/j.earscirev.2016.12.005 · Cited by 187
Compound Extremes in Hydroclimatology: A Review
Hao, Z., Singh, V. P., & Hao, F. (2018) · Water · doi:10.3390/w10060718 · Cited by 187
Ulbrich, U., Lionello, P., Belušić, D., Jacobeit, J., Knippertz, P., Kuglitsch, F. G., Leckebusch, G. C., Luterbacher, J., Maugeri, M., Maheras, P., et al. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-416042-2.00005-7 · Cited by 186
Rainfall extremes: Toward reconciliation after the battle of distributions
Serinaldi, F., & Kilsby, C. G. (2014) · Water Resources Research · doi:10.1002/2013wr014211 · Cited by 186
Davison, A. C., & Huser, R. (2015) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010814-020133 · Cited by 186
19 The World Distribution of Household Wealth
Davies, J., Sandström, S., Shorrocks, A. F., & Wolff, E. N. (2008) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199548880.003.0019 · Cited by 186
Climatology Models for Extreme Hurricane Winds near the United States
Jagger, T. H., & Elsner, J. B. (2006) · Journal of Climate · doi:10.1175/jcli3913.1 · Cited by 186
Simulating extreme total water levels using a time‐dependent, extreme value approach
Serafin, K. A., & Ruggiero, P. (2014) · Journal of Geophysical Research Oceans · doi:10.1002/2014jc010093 · Cited by 188
Chan, S. C., Kendon, E., Fowler, H. J., Blenkinsop, S., Roberts, N. M., & Ferro, C. A. T. (2014) · Journal of Climate · doi:10.1175/jcli-d-13-00723.1 · Cited by 182
Davison, A. C., & Gholam‐Rezaee, M. (2011) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2011.0412 · Cited by 182
Vicente-Serrano, S. M., Beguerı́a, S., Gimeno, L., Eklundh, L., Giuliani, G., Weston, D., Kenawy, A. E., López‐Moreno, J. I., Nieto, R., Ayenew, T., et al. (2012) · Applied Geography · doi:10.1016/j.apgeog.2012.02.001 · Cited by 188
Climate Change and Changes in Compound Coastal‐Riverine Flooding Hazard Along the U.S. Coasts
Ghanbari, M., Arabi, M., Kao, S., Obeysekera, J. T. B., & Sweet, W. (2021) · Earth s Future · doi:10.1029/2021ef002055 · Cited by 181
Cliver, E. W., Schrijver, C. J., Shibata, K., & USOSKIN, I. G. (2022) · Living Reviews in Solar Physics · doi:10.1007/s41116-022-00033-8 · Cited by 216
Estimating value-at-risk: a point process approach
Chavez‐Demoulin, V., Davison, A. C., & McNeil, A. J. (2005) · Quantitative Finance · doi:10.1080/14697680500039613 · Cited by 180
The impact of sea level rise on storm surge water levels in the northern part of the German Bight
Arns, A., Wahl, T., Dangendorf, S., & Jensen, J. (2015) · Coastal Engineering · doi:10.1016/j.coastaleng.2014.12.002 · Cited by 180
A simple technique for estimating an allowance for uncertain sea-level rise
Hunter, J. R. (2011) · Climatic Change · doi:10.1007/s10584-011-0332-1 · Cited by 180
Flood hazard in Europe in an ensemble of regional climate scenarios
Dankers, R., & Feyen, L. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2008jd011523 · Cited by 179
Variograms for spatial max-stable random fields
Cooley, D., Naveau, P., & Poncet, P. (2006) · Lecture notes in statistics · doi:10.1007/0-387-36062-x_17 · Cited by 179
Inferring extinction from a sighting record
Solow, A. R. (2005) · Mathematical Biosciences · doi:10.1016/j.mbs.2005.02.001 · Cited by 178
Igba, J., Alemzadeh, K., Durugbo, C., & Eiriksson, E. T. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.01.006 · Cited by 177
Determining the Probability of Project Cost Overruns
Love, P. E. D., Wang, X., Sing, C.-P., & Tiong, R. L. K. (2012) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0000575 · Cited by 185
Groisman, P., & Knight, R. W. (2008) · Journal of Climate · doi:10.1175/2007jcli2013.1 · Cited by 177
Ghosh, S., Hari, V., Sharma, T., Karmakar, S., Kasiviswanathan, K. S., Dhanesh, Y., Sudheer, K. P., & Gunthe, S. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158670 · Cited by 177
The legacy of episodic climatic events in shaping temperate, broadleaf forests
Pederson, N., Dyer, J. M., McEwan, R. W., Hessl, A., Mock, C. J., Orwig, D. A., Rieder, H. E., & Cook, B. I. (2014) · Ecological Monographs · doi:10.1890/13-1025.1 · Cited by 177
Goiri, Í., Bianchini, R., Nagarakatte, S., & Nguyen, T. D. (2015) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/2694344.2694351 · Cited by 176
Amplification of flood frequencies with local sea level rise and emerging flood regimes
Buchanan, M. K., Oppenheimer, M., & Kopp, R. E. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa6cb3 · Cited by 184
Recurrence rates of large explosive volcanic eruptions
Deligne, N. I., Coles, S., & Sparks, R. S. J. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jb006554 · Cited by 175
Self-citationToreti, A., Xoplaki, E., Maraun, D., Kuglitsch, F. G., Wanner, H., & Luterbacher, J. (2010) · Natural hazards and earth system sciences · doi:10.5194/nhess-10-1037-2010 · Cited by 175
Zheng, L., Sayed, T., & Essa, M. (2018) · Accident Analysis & Prevention · doi:10.1016/j.aap.2018.12.007 · Cited by 173
Spatial and temporal pattern of wildfires in California from 2000 to 2019
Li, S., & Banerjee, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-88131-9 · Cited by 173
On the relation between weather-related disaster impacts, vulnerability and climate change
Visser, H., Petersen, A. C., & Ligtvoet, W. (2014) · Climatic Change · doi:10.1007/s10584-014-1179-z · Cited by 172
Fitting the generalized Pareto distribution to data using maximum goodness-of-fit estimators
Luceño, A. (2005) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2005.09.011 · Cited by 171
Definition, diagnosis, and origin of extreme weather and climate events
Stephenson, D. B. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511535840.004 · Cited by 171
A global analysis of the asymmetric effect of ENSO on extreme precipitation
Sun, X., Renard, B., Thyer, M., Westra, S., & Lang, M. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.09.016 · Cited by 170
Spatial modeling of extreme snow depth
Blanchet, J., & Davison, A. C. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas464 · Cited by 170
Serafin, K. A., Ruggiero, P., & Stockdon, H. F. (2017) · Geophysical Research Letters · doi:10.1002/2016gl071020 · Cited by 170
The Structure of Climate Variability Across Scales
Franzke, C. L. E., Barbosa, S., Blender, R., Fredriksen, H., Laepple, T., Lambert, F., Nilsen, T., Rypdal, K., Rypdal, M. W., Scotto, M. G., et al. (2020) · Reviews of Geophysics · doi:10.1029/2019rg000657 · Cited by 170
Evaluation of multiple regional climate models for summer climate extremes over East Asia
Park, C., Min, S., Lee, D., Cha, D., Suh, M., Kang, H., Hong, S., Lee, D., Baek, H., Boo, K., et al. (2015) · Climate Dynamics · doi:10.1007/s00382-015-2713-z · Cited by 168
Jump tails, extreme dependencies, and the distribution of stock returns
Bollerslev, T., Todorov, V., & Li, S. Z. (2012) · Journal of Econometrics · doi:10.1016/j.jeconom.2012.08.014 · Cited by 168
Ping, L., & Ding, Y. (2017) · Advances in Atmospheric Sciences · doi:10.1007/s00376-016-6120-0 · Cited by 167
On the prediction of extreme ecological events
Denny, M. W., Hunt, L. J. H., Miller, L. P., & Harley, C. D. G. (2009) · Ecological Monographs · doi:10.1890/08-0579.1 · Cited by 166
Nonstationary modeling of a long record of rainfall and temperature over Rome
Villarini, G., Smith, J. A., & Napolitano, F. (2010) · Advances in Water Resources · doi:10.1016/j.advwatres.2010.03.013 · Cited by 166
Krysanova, V., Vetter, T., Eisner, S., Huang, S., Pechlivanidis, I. G., Strauch, M., Gelfan, A. N., Kumar, R., Aich, V., Arheimer, B., et al. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa8359 · Cited by 166
Trends in Europe storm surge extremes match the rate of sea-level rise
Calafat, F. M., Wahl, T., Tadesse, M. G., & Sparrow, S. (2022) · Nature · doi:10.1038/s41586-022-04426-5 · Cited by 166
Probabilistic flood risk mapping including spatial dependence
Neal, J. C., Keef, C., Bates, P., Beven, K. J., & Leedal, D. T. (2012) · Hydrological Processes · doi:10.1002/hyp.9572 · Cited by 165
Haigh, I. D., MacPherson, L. R., Mason, M. S., Wijeratne, S., Pattiaratchi, C. B., Crompton, R. P., & George, S. E. (2013) · Climate Dynamics · doi:10.1007/s00382-012-1653-0 · Cited by 165
Resampling strategies for regression
Torgo, L., Branco, P., Ribeiro, R. P., & Pfahringer, B. (2014) · Expert Systems · doi:10.1111/exsy.12081 · Cited by 169
Interpreting historical, botanical, and geological evidence to aid preparations for future floods
Wilhelm, B., Ballesteros‐Cánovas, J. A., Macdonald, N., Toonen, W. H. J., Baker, V. R., Barriendos, M., Benito, G., Brauer, A., Corella, J. P., Denniston, R. F., et al. (2018) · Wiley Interdisciplinary Reviews Water · doi:10.1002/wat2.1318 · Cited by 165
Detection and attribution of large spatiotemporal extreme events in Earth observation data
Jakob Zscheischler, A., Mahecha, M. D., Stefan Harmeling, B., & Reichstein, M. (2013) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.03.004 · Cited by 167
Non-stationary regional flood frequency analysis at ungauged sites
Leclerc, M., & Ouarda, T. B. M. J. (2007) · Journal of Hydrology · doi:10.1016/j.jhydrol.2007.06.021 · Cited by 164
Changing characteristics of extreme wet and dry spells of Indian monsoon rainfall
Rajendran, V., & Dhanya, C. T. (2016) · Journal of Geophysical Research Atmospheres · doi:10.1002/2015jd024310 · Cited by 165
Statistics of Extremes, with Applications in Environment, Insurance, and Finance
Smith, R. L. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203483350-5 · Cited by 164
Forecaster's Dilemma: Extreme Events and Forecast Evaluation
Lerch, S., Thorarinsdottir, T., Ravazzolo, F., & Gneiting, T. (2015) · Statistical Science · doi:10.1214/16-sts588 · Cited by 191
Tabari, H. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125932 · Cited by 173
Modelling Non-Stationary Extremes with Application to Surface Level Ozone
Eastoe, E., & Tawn, J. A. (2008) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2008.00638.x · Cited by 162
A practical method to identify displaced and split stratospheric polar vortex events
Seviour, W. J. M., Mitchell, D., & Gray, L. J. (2013) · Geophysical Research Letters · doi:10.1002/grl.50927 · Cited by 162
Personal Wealth from a Global Perspective
(2008) · doi:10.1093/acprof:oso/9780199548880.001.0001 · Cited by 163
Herrera, S., Fernández, J., & Gutiérrez, J. M. (2015) · International Journal of Climatology · doi:10.1002/joc.4391 · Cited by 161
Examining Flood Frequency Distributions in the Midwest U.S.1
Villarini, G., Smith, J. A., Baeck, M. L., & Krajewski, W. F. (2011) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.2011.00540.x · Cited by 161
Ragno, E., AghaKouchak, A., Love, C. A., Cheng, L., Vahedifard, F., & Lima, C. (2018) · Water Resources Research · doi:10.1002/2017wr021975 · Cited by 161
Natural hazards and extreme events in the Baltic Sea region
Rutgersson, A., Kjellström, E., Haapala, J. J., Stendel, M., Данилович, И. С., Drews, M., Jylhä, K., Kujala, P., Larsén, X. G., Halsnæs, K., et al. (2022) · Earth System Dynamics · doi:10.5194/esd-13-251-2022 · Cited by 178
Anticipating Catastrophes through Extreme Value Modelling
Coles, S., & Pericchi, L. R. (2003) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00413 · Cited by 161
Self-citationOddou‐Muratorio, S., Klein, E. K., & Austerlitz, F. (2005) · Molecular Ecology · doi:10.1111/j.1365-294x.2005.02720.x · Cited by 160
Observed extreme precipitation trends and scaling in Central Europe
Zeder, J., & Fischer, E. M. (2020) · Weather and Climate Extremes · doi:10.1016/j.wace.2020.100266 · Cited by 171
Automated threshold selection methods for extreme wave analysis
Thompson, P. A., Cai, Y., Reeve, D. E., & Stander, J. (2009) · Coastal Engineering · doi:10.1016/j.coastaleng.2009.06.003 · Cited by 159
The extreme 2013/2014 winter storms: Beach recovery along the southwest coast of England
Scott, T., Masselink, G., O'Hare, T. J., Saulter, A., Poate, T., Russell, P., Davidson, M., & Conley, D. (2016) · Marine Geology · doi:10.1016/j.margeo.2016.10.011 · Cited by 159
The return period of wind storms over Europe
Della-Marta, P. M., Mathis, H., Frei, C., Liniger, M. A., Kleinn, J., & Appenzeller, C. (2008) · International Journal of Climatology · doi:10.1002/joc.1794 · Cited by 164
Gosling, S. N., McGregor, G. R., & Lowe, J. A. (2008) · International Journal of Biometeorology · doi:10.1007/s00484-008-0189-9 · Cited by 158
Kingsolver, J. G., & Buckley, L. B. (2017) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2016.0147 · Cited by 162
Beguerı́a, S., Angulo‐Martínez, M., Vicente-Serrano, S. M., López‐Moreno, J. I., & Kenawy, A. E. (2010) · International Journal of Climatology · doi:10.1002/joc.2218 · Cited by 157
Xu, L., Wang, S., & Tang, R. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.01.022 · Cited by 157
Estimating Local Intrinsic Dimensionality
Amsaleg, L., Chelly, O., Furon, T., Girard, S., Houle, M. E., Kawarabayashi, K., & Nett, M. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783405 · Cited by 161
Estimating sea-level extremes under conditions of uncertain sea-level rise
Hunter, J. R. (2009) · Climatic Change · doi:10.1007/s10584-009-9671-6 · Cited by 155
Allowances for evolving coastal flood risk under uncertain local sea-level rise
Buchanan, M. K., Kopp, R. E., Oppenheimer, M., & Tebaldi, C. (2016) · Climatic Change · doi:10.1007/s10584-016-1664-7 · Cited by 155
Hydro-meteorological risk assessment methods and management by nature-based solutions
Sahani, J., Kumar, P., Debele, S., Spyrou, C., Loupis, M., Aragão, L., Porcù, F., Shah, M. A. R., & Di Sabatino, S. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.133936 · Cited by 154
Towler, E., Rajagopalan, B., Gilleland, E., Summers, R. S., Yates, D. N., & Katz, R. W. (2010) · Water Resources Research · doi:10.1029/2009wr008876 · Cited by 154
Investigation of trends in extreme value wave height and wind speed
Young, I. R., Vinoth, J., Zieger, S., & Babanin, A. V. (2012) · Journal of Geophysical Research Atmospheres · doi:10.1029/2011jc007753 · Cited by 154
Design considerations for riverine floods in a changing climate – A review
François, B., Schlef, K., Wi, S., & Brown, C. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.068 · Cited by 164
The extreme dependency score: a non‐vanishing measure for forecasts of rare events
Stephenson, D. B., Casati, B., Ferro, C. A. T., & Wilson, C. A. (2008) · Meteorological Applications · doi:10.1002/met.53 · Cited by 154
Robustness of Future Changes in Local Precipitation Extremes
Kendon, E., Rowell, D. P., Jones, R., & Buonomo, E. (2008) · Journal of Climate · doi:10.1175/2008jcli2082.1 · Cited by 153
Recent severe heat waves in central Europe: how to view them in a long‐term prospect?
Kyselý, J. (2009) · International Journal of Climatology · doi:10.1002/joc.1874 · Cited by 153
Buonomo, E., Jones, R., Huntingford, C., & Hannaford, J. (2007) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.13 · Cited by 153
Clustering of Regional-Scale Extreme Precipitation Events in Southern Switzerland
Barton, Y., Giannakaki, P., von Waldow, H., Chevalier, C., Pfahl, S., & Martius, O. (2015) · Monthly Weather Review · doi:10.1175/mwr-d-15-0205.1 · Cited by 153
Return period and risk analysis of nonstationary low-flow series under climate change
Du, T., Xiong, L., Xu, C., Gippel, C. J., Guo, S., & Liu, P. (2015) · Journal of Hydrology · doi:10.1016/j.jhydrol.2015.04.041 · Cited by 156
Pareto Models, Top Incomes and Recent Trends in UK Income Inequality
Jenkins, S. P. (2016) · Economica · doi:10.1111/ecca.12217 · Cited by 153
Future behavior of wind wave extremes due to climate change
Lobeto, H., Menéndez, M., & Losada, Í. J. (2021) · Scientific Reports · doi:10.1038/s41598-021-86524-4 · Cited by 153
Rojas, R., Feyen, L., Dosio, A., & Bavera, D. (2011) · Hydrology and earth system sciences · doi:10.5194/hess-15-2599-2011 · Cited by 152
The impact of climate change on rainfall Intensity–Duration–Frequency (IDF) curves in Alabama
Mirhosseini, G., Srivastava, P., & Stefanova, L. (2012) · Regional Environmental Change · doi:10.1007/s10113-012-0375-5 · Cited by 151
Extreme precipitation and phosphorus loads from two agricultural watersheds
Carpenter, S. R., Booth, E. G., & Kucharik, C. J. (2017) · Limnology and Oceanography · doi:10.1002/lno.10767 · Cited by 151
Statistical Modeling of Extreme Rainfall in Southwest Western Australia
Li, Y., Cai, W., & Campbell, E. P. (2005) · Journal of Climate · doi:10.1175/jcli-3296.1 · Cited by 150
Analyses of seasonal and annual maximum daily discharge records for central Europe
Villarini, G., Smith, J. A., Serinaldi, F., & Ntelekos, A. A. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.01.007 · Cited by 150
Influence of climate variability on seasonal extremes over Australia
Min, S., Cai, W., & Whetton, P. H. (2013) · Journal of Geophysical Research Atmospheres · doi:10.1002/jgrd.50164 · Cited by 148
RNA snp: Efficient Detection of Local RNA Secondary Structure Changes Induced by SNP s
Sabarinathan, R., Tafer, H., Seemann, S. E., Hofacker, I. L., Stadler, P. F., & Gorodkin, J. (2013) · Human Mutation · doi:10.1002/humu.22273 · Cited by 151
Half a degree and rapid socioeconomic development matter for heatwave risk
Russo, S., Sillmann, J., Sippel, S., Barcikowska, M. J., Ghisetti, C., Smid, M., & O’Neill, B. C. (2019) · Nature Communications · doi:10.1038/s41467-018-08070-4 · Cited by 152
Diametric changes in trends and patterns of extreme rainfall over India from pre‐1950 to post‐1950
Hari, V., Karmakar, S., & Ghosh, S. (2013) · Geophysical Research Letters · doi:10.1002/grl.50631 · Cited by 147
Landslide rupture and the probability distribution of mobilized debris volumes
Stark, C. P., & Guzzetti, F. (2009) · Journal of Geophysical Research Atmospheres · doi:10.1029/2008jf001008 · Cited by 147
Projection and Analysis of Extreme Wave Climate
Caires, S., Swail, V. R., & Wang, X. L. (2006) · Journal of Climate · doi:10.1175/jcli3918.1 · Cited by 147
Yilmaz, A. G., Hossain, I., & Perera, B. J. C. (2014) · Hydrology and earth system sciences · doi:10.5194/hess-18-4065-2014 · Cited by 147
Estimating Mean Dimensionality of Analysis of Variance Decompositions
Liu, R., & Owen, A. B. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001410 · Cited by 147
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
