The convex composition of two copulas (Joe, 2014, p. 155) provides for some simple complexity extension between copula families. Let \(\mathbf{A}\) and \(\mathbf{B}\) be copulas with respective vectors of parameters \(\Theta_\mathbf{A}\) and \(\Theta_\mathbf{B}\), then the convex combination of these copulas is
$$\mathbf{C}^{\times}_{\alpha}(u,v) = \alpha\cdot\mathbf{A}(u, v; \Theta_\mathbf{A}) - (1-\alpha)\cdot\mathbf{B}(u,v; \Theta_\mathbf{B})\mbox{,}$$
where \(0 \le \alpha \le 1\). The generalization of this function for \(N\) number of copulas is provided by convexCOP
.
convex2COP(u,v, para, ...)
Value(s) for the convex combination copula is returned.
Nonexceedance probability \(u\) in the \(X\) direction;
Nonexceedance probability \(v\) in the \(Y\) direction;
A special parameter list
(see Note); and
Additional arguments to pass to the copula.
W.H. Asquith
Joe, H., 2014, Dependence modeling with copulas: Boca Raton, CRC Press, 462 p.
COP
, breveCOP
, convexCOP
, composite1COP
, composite2COP
, composite3COP
, FRECHETcop
,
glueCOP