public class GeneralizedEVD extends Object implements UnivariateEVD
evd::dgev, evd::pgev, evd::qgev, evd::mtransform.| Constructor and Description |
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GeneralizedEVD()
Create an instance of generalized extreme value distribution with the default parameter
values: location \(\mu=0\), scale \(\sigma=1\), shape \(\xi=0\).
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GeneralizedEVD(double location,
double scale,
double shape)
Create an instance of generalized extreme value distribution with the given parameters.
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| Modifier and Type | Method and Description |
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double |
cdf(double x)
Gets the cumulative probability F(x) = Pr(X ≤ x).
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double |
density(double x)
The density function, which, if exists, is the derivative of F.
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double |
entropy()
Gets the entropy of this distribution.
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double |
getLocation()
Get the location parameter.
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double |
getScale()
Get the scale parameter.
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double |
getShape()
Get the shape parameter.
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double |
kurtosis()
Gets the excess kurtosis of this distribution.
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double |
logDensity(double x)
Get the logarithm of the probability density function at \(x\), that is, \(\log(f(x))\).
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double |
marginalInverseTransform(double x)
Inverse of marginal transform.
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double |
marginalTransform(double x)
Transform to exponential margins under the GEV model.
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double |
mean()
Gets the mean of this distribution.
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double |
median()
Gets the median of this distribution.
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double |
moment(double x)
The moment generating function is the expected value of etX.
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double |
quantile(double p)
Gets the quantile, the inverse of the cumulative distribution function.
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double |
skew()
Gets the skewness of this distribution.
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double |
variance()
Gets the variance of this distribution.
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public GeneralizedEVD()
public GeneralizedEVD(double location,
double scale,
double shape)
location - the location parameter \(\mu\)scale - the scale parameter \(\sigma > 0\)shape - the shape parameter \(\xi\)public double getLocation()
public double getScale()
public double getShape()
public double marginalTransform(double x)
x - \(x\)public double marginalInverseTransform(double x)
x - \(x\)public double cdf(double x)
cdf in interface ProbabilityDistributionx - \(x\)public double density(double x)
f(x) = dF(X) / dxThis may not always exist. For the discrete cases, this is the probability mass function. It gives the probability that a discrete random variable is exactly equal to some value. The probability density function of GEV distribution is \[ f(x;\mu,\sigma,\xi) = \frac{1}{\sigma}\left[1+\xi\left(\frac{x-\mu}{\sigma}\right)\right]^{(-1/\xi)-1} \exp\left\{-\left[1+\xi\left(\frac{x-\mu}{\sigma}\right)\right]^{-1/\xi}\right\} \] for \(1+\xi(x-\mu)/\sigma>0\).
density in interface ProbabilityDistributionx - \(x\)public double logDensity(double x)
UnivariateEVDlogDensity in interface UnivariateEVDx - \(x\)public double quantile(double p)
ProbabilityDistributionThis may not always exist.F-1(u) = x, such that Pr(X ≤ x) = u
quantile in interface ProbabilityDistributionp - u, a quantilepublic double mean()
ProbabilityDistributionmean in interface ProbabilityDistributionpublic double moment(double x)
ProbabilityDistributionE(etX)This may not always exist.
moment in interface ProbabilityDistributionx - tpublic double skew()
ProbabilityDistributionskew in interface ProbabilityDistributionpublic double variance()
ProbabilityDistributionvariance in interface ProbabilityDistributionpublic double median()
ProbabilityDistributionmedian in interface ProbabilityDistributionpublic double kurtosis()
ProbabilityDistributionkurtosis in interface ProbabilityDistributionpublic double entropy()
ProbabilityDistributionentropy in interface ProbabilityDistributionCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.