An Introduction to Statistical Extreme Value Theory - IMAGe

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Uncertainty (GEV)Essentially, the maximum likelihood approach yieldsstandard errors for the estimates.These errors can be propagated to the return levels:Return Level100 200 300 400 500 6000.1 1 10 100 1000Return PeriodTutorial in Extreme Value Theory
Uncertainty (GPD)More data means less uncertainty.From the GPD (threshold model) fit for dailyprecipitation in Boulder:ξ = 0.22, 95% conf. interval is (-0.12,0.16).σ = 51.46, 95% conf. interval is (40.70, 62.21).Tutorial in Extreme Value Theory
Page 1 and 2: An Introduction toStatistical Extre
Page 3 and 4: FundamentalsIn classical statistics
Page 5 and 6: FundamentalsIn extreme value theory
Page 7 and 8: Block Maxima ApproachModel extreme
Page 9 and 10: Fitting a GEV - Estimating Paramete
Page 11 and 12: Fitting a GEV - Return LevelsOften
Page 13 and 14: Threshold ModelsModel exceedances o
Page 15 and 16: Threshold ModelsModel exceedances o
Page 17 and 18: Fitting a GPD - Estimating Paramete
Page 19 and 20: Fitting a GPD - Choosing a Threshol
Page 21 and 22: Fitting a GPD - Choosing a Threshol
Page 23: Uncertainty (GEV)Essentially, the m
Page 27 and 28: Dependence - DeclusteringFor the GE
Page 29 and 30: Dependence - (fitting the GPD)Choos
Page 31 and 32: Dependence - (fitting the GPD)Modif
Page 33 and 34: Dependence - (fitting the GPD)Use u
Page 35 and 36: SeasonalityTo incorporate seasonali

An Introduction to Statistical Extreme Value Theory - IMAGe

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