Perform copula multiplication (so-called “\(\ast\)-product” or Markov Product) (Darsow and others, 1992) is a continuous analog of matrix multiplication and yields another copula:
$$\bigl(\mathbf{C}_1 \ast \mathbf{C}_2 \bigr)(u,v) = \mathbf{C}_3(u,v) = \int_\mathcal{I} \frac{\delta \mathbf{C}_1(u, t)}{\delta v} \frac{\delta \mathbf{C}_2(t, v)}{\delta u}\,\mathrm{d}t\mbox{,}$$
for copulas \(\mathbf{C}_1(u, v)\) and \(\mathbf{C}_2(u, v)\) are copulas whose \(\ast\)-product yields copula \(\mathbf{C}_3(u, v)\) in terms of partial derivatives (derCOP
and derCOP2
) of the other two. Nelsen (2006, p. 245) lists several identities of the \(\ast\)-product involving the product (\(\mathbf{\Pi}\); P
), lower bound (\(\mathbf{W}\); W
), and upper bound (\(\mathbf{M}\); M
) copulas:
$$\mathbf{\Pi} \ast \mathbf{C} = \mathbf{C} \ast \mathbf{\Pi} = \mathbf{\Pi}\mbox{,}$$
$$\mathbf{M} \ast \mathbf{C} = \mathbf{C} \ast \mathbf{M} = \mathbf{M}\mbox{,}$$
$$\bigl(\mathbf{W} \ast \mathbf{C}\bigr)(u,v) = v - \mathbf{C}(1-u, v)\mbox{\ and\ } \bigl(\mathbf{C} \ast \mathbf{W}\bigr)(u,v) = u - \mathbf{C}(u, 1-v)\mbox{, and}$$
$$\mathbf{W} \ast \mathbf{W} = \mathbf{M}\mbox{ and } \mathbf{W} \ast \mathbf{C} \ast \mathbf{W} = \hat{\mathbf{C}}\mbox{,}$$
where \(\hat{\mathbf{C}}\) is the survival copula (surCOP
). The \(\ast\)-product is associative:
$$\mathbf{A} \ast (\mathbf{B} \ast \mathbf{C}) = (\mathbf{A} \ast \mathbf{B}) \ast \mathbf{C}\mbox{,}$$
but \(\ast\)-product is not commutative (order independent). Nelsen (2006, p. 245) reports that “if we view \(\ast\) as a binary operation on the set of copulas, then \(\mathbf{\Pi}\) is the null element, and \(\mathbf{M}\) is the identity.” Copula mulitiplication is closely linked to Markov Processes (Nelsen, 2006, pp. 244--248).
For other descriptions and computations of copula combination are possible using the copBasic package, see convexCOP
, convex2COP
, composite1COP
, composite2COP
, composite3COP
, glueCOP
, and convexCOP
.
prod2COP(u,v, cop1=NULL, para1=NULL, cop2=NULL, para2=NULL, para=NULL,
pinterval=NULL, ...)
Value(s) for the copula are returned.
Nonexceedance probability \(u\) in the \(X\) direction;
Nonexceedance probability \(v\) in the \(Y\) direction;
The \(\mathbf{C}_1(u,v; \Theta_1)\) copula function with vectorization as in asCOP
;
Vector of parameters or other data structures for \(\Theta_1\), if needed, to pass to copula \(\mathbf{C}_1(u,v; \Theta_1)\);
The \(\mathbf{C}_2(u,v; \Theta_2)\) copula function with vectorization as in asCOP
;
Vector of parameters or other data structures for \(\Theta_2\), if needed, to pass to copula \(\mathbf{C}_2(u,v; \Theta_2)\);
An R list
that can take the place of the cop1
, para1
, cop2
, and para2
arguments. These four will be populated from same named elements of the list
, and if the other four arguments were specified through the function interface, these are silently ignored;
An optional interval for the above integral. The default is \(\mathcal{I} = [0,1]\) but the option of the user to replace exact end points with “small” numbers is possible (e.g. interval=
c(lo, 1-lo)
for say lo=.Machine$double.eps
). This interval is uniquely picked up for the interval in the above definition of prod2COP
. The pinterval
can also be set within the para
and the function will pick it up from there; and
Additional arguments to pass to the copulas.
W.H. Asquith
Darsow, W.F., Nguyen, B., and Olsen, E.T., 1992, Copulas and Markov processes: Illinois Journal of Mathematics, v. 26, pp. 600--624, tools:::Rd_expr_doi("10.1215/IJM/1255987328").
Nelsen, R.B., 2006, An introduction to copulas: New York, Springer, 269 p.
COP
, composite1COP
, composite2COP
, composite3COP
,
convexCOP
, convex2COP
, glueCOP