# SuanShu, a Java numerical and statistical library

com.numericalmethod.suanshu.stats.evt.evd.bivariate

## Interface BivariateEVD

• ### Method Summary

All Methods
Modifier and Type Method and Description
double conditionalCopula(double x1, double x2)
The conditional copula function conditioning on either margin.
double dependence(double x)
The dependence function $$A$$ for the parametric bivariate extreme value model.
double spectralDensity(double x)
The density $$h$$ of the spectral measure $$H$$ on the interval (0,1).
• ### Methods inherited from interface com.numericalmethod.suanshu.stats.distribution.multivariate.BivariateProbabilityDistribution

cdf, density
• ### Methods inherited from interface com.numericalmethod.suanshu.stats.distribution.multivariate.MultivariateProbabilityDistribution

cdf, covariance, density, entropy, mean, mode, moment
• ### Methods inherited from interface com.numericalmethod.suanshu.stats.random.rng.multivariate.RandomVectorGenerator

nextVector
• ### Methods inherited from interface com.numericalmethod.suanshu.stats.random.Seedable

seed
• ### Method Detail

• #### spectralDensity

double spectralDensity(double x)
The density $$h$$ of the spectral measure $$H$$ on the interval (0,1). Any bivariate extreme value distribution can be written as $G(z_1,z_2) = \exp\left\{-\int_0^1 \max(w y_1, (1-w) y_2) H(dw)\right\}$ where $$y_i=(1+\xi_i(z_i-\mu_i)/\sigma_i)^{(-1/\xi_i)}$$, and $$\mu_i$$, $$\sigma_i$$, $$\xi_i$$ are the location, scale and shape parameters.

For some function $$H()$$ defined on [0,1], satisfying $\int_0^1 w H(dw) = \int_0^1 (1-w) H(dw) = 1.$ $$H()$$ is called the spectral measure, with density $$h$$ on the interval (0,1).

For differentiable models, $$H$$ may have up to two point masses: at zero and one. Assuming that the model parameters are in the interior of the parameter space, we have the following. For the asymmetric logistic and asymmetric negative logistic models the point masses are of size $$(1-t_1)$$ and $$(1-t_2)$$ respectively. For the asymmetric mixed model they are of size $$(1-\alpha-\beta)$$ and $$(1-\alpha-2*\beta)$$ respectively. For all other models the point masses are zero.

At independence, $$H$$ has point masses of size one at both zero and one. At complete dependence [a non-differentiable model] $$H$$ has a single point mass of size two at 1/2. In either case, $$h$$ is zero everywhere.

Parameters:
x - x
Returns:
$$h(x)$$
• #### dependence

double dependence(double x)
The dependence function $$A$$ for the parametric bivariate extreme value model. Any bivariate extreme value distribution can be written as $G(z_1,z_2) = \exp\left\{-(y_1+y_2)A\left[y_1/(y_1+y_2)\right]\right\}$ for some function $$A()$$ defined on [0,1], where $$y_i=(1+\xi_i(z_i-\mu_i)/\sigma_i)^{(-1/\xi_i)}$$, and $$\mu_i$$, $$\sigma_i$$, $$\xi_i$$ are the location, scale and shape parameters.

It follows that $$A(0)=A(1)=1$$, and that $$A()$$ is a convex function with $$\max(x,1-x) \le A(x) \le 1$$ for all $$0 \le x \le 1$$.

The lower and upper limits of $$A$$ are obtained under complete dependence and independence respectively. $$A()$$ does not depend on the marginal parameters.

Parameters:
x - x
Returns:
$$A(x)$$
• #### conditionalCopula

double conditionalCopula(double x1,
double x2)
The conditional copula function conditioning on either margin. The function calculates $$P(U_1 < x_1|U_2 = x_2)$$, where $$(U_1,U_2)$$ is a random vector with Uniform(0,1) margins and with a dependence structure given by the specified parametric model.
Parameters:
x1 - an observation from $$U_1$$
x2 - an observation from $$U_2$$
Returns:
the conditional copula $$P(U_1 < x_1|U_2 = x_2)$$