Description Usage Arguments Methods References
The h function represents the conditional distribution function of a bivariate copula and it should be defined for every copula used in a pair-copula construction. It is defined as the partial derivative of the distribution function of the copula w.r.t. the second argument h(x,v) = F(x|v) = \partial C(x,v) / \partial v.
1 | h(copula, x, v, eps)
|
copula |
A bivariate |
x |
Numeric vector with values in [0,1]. |
v |
Numeric vector with values in [0,1]. |
eps |
To avoid numerical problems for extreme values, the values of
|
signature(copula = "copula")
Default definition of the h function for a bivariate copula.
This method is used if no particular definition is given for a copula.
The partial derivative is calculated numerically using the
numericDeriv
function.
signature(copula = "indepCopula")
The h function of the independence copula.
signature(copula = "normalCopula")
The h function of the normal copula.
signature(copula = "tCopula")
The h function of the t copula.
signature(copula = "claytonCopula")
The h function of the Clayton copula.
signature(copula = "gumbelCopula")
The h function of the Gumbel copula.
signature(copula = "fgmCopula")
The h function of the Farlie-Gumbel-Morgenstern copula.
signature(copula = "frankCopula")
The h function of the Frank copula.
signature(copula = "galambosCopula")
The h function of the Galambos copula.
Aas, K. and Czado, C. and Frigessi, A. and Bakken, H. (2009) Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44, 182–198.
Schirmacher, D. and Schirmacher, E. (2008) Multivariate dependence modeling using pair-copulas. Enterprise Risk Management Symposium, Chicago.
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