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#Com.numericalmethod.suanshu.stats.evt.evd.univariate Classes and Interfaces - 32 results found.
NameDescriptionTypePackageFramework
ACERAnalysisAverage Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima (M) distribution from observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERAnalysis .ResultClasscom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERByCountingEstimate epsilons by counting conditional exceedances from the observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
ACERConfidenceIntervalUsing the given (estimated) ACER function as the mean, find the ACER parameters at the lower and upper bounds of the estimated confidence interval of ACER values.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERFunctionThe ACER (Average Conditional Exceedance Rate) function (epsilon_k(eta)) approximates the epsilon_k(eta) = Pr(X_k > eta Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERFunction .ACERParameterParameters for ACERFunction.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERInverseFunctionThe inverse of the ACER function.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERLogFunctionThe ACER function in log scale (base e), i.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERReturnLevelGiven an ACER function, compute the return level (eta) for a given return period (R).Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
ACERUtilsUtility functions used in ACER empirical analysis.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
ConfidenceIntervalThis class stores information for a list of confidence intervals, with the same confidence level.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShu
EmpiricalACERThis class contains empirical ACER (hat{epsilon_k}(eta_i)) values and other related statistics estimated from observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
EmpiricalACEREstimationThis class estimates empirical ACER values from the given observations.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
EmpiricalACERStatisticsThis class contains the computed statistics of the estimated ACERs.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
EpsilonStatisticsCalculatorCompute statistics: mean, confidence interval of estimated ACER values (epsilon_k(eta_i)).Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empiricalSuanShu
FrechetDistributionClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
GeneralizedEVDClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
GeneralizedParetoDistributionGeneralized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
GEVFittingByMaximumLikelihoodEstimate the GeneralizedEVD parameter from the observations by maximum likelihood approach.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShu
GumbelDistributionThe Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, The cumulative distribution function isClasscom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
InverseTransformSamplingEVDRNGGenerate random numbers according to a given univariate extreme value distribution, by inverse transform sampling.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.rngSuanShu
LinearFitFind the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
MaximaDistributionThe distribution of (M), where (M=max(x_1,x_2,.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
MaximumLikelihoodFittingThis interface defines model fitting by maximum likelihood algorithm.Interfacecom.numericalmethod.suanshu.stats.evt.evd.univariate.fittingSuanShu
MinimaDistributionThe distribution of (M), where (M=min(x_1,x_2,.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
NonlinearFitFit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
NonlinearFit .ResultClasscom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acerSuanShu
OrderStatisticsDistributionThe asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic.Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
PeaksOverThresholdPeaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution (GPD) using maximum likelihood on the observations that are over a given threshold.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.potSuanShu
PeaksOverThresholdOnClustersSimilar to POT, but only use the peak observations in clusters for the parametric estimation.Classcom.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.potSuanShu
ReversedWeibullDistributionThe Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with (xi<0).Classcom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu
UnivariateEVDDistribution of extreme values (e.Interfacecom.numericalmethod.suanshu.stats.evt.evd.univariateSuanShu