The gluing copula technique (Erdely, 2017, p. 71), given two bivariate copulas \(\mathbf{C}_A\) and \(\mathbf{C}_B\) and a fixed value \(0 \le \gamma \le 1\) is $$\mathbf{C}_{\gamma}(u,v) = \gamma\cdot\mathbf{C}_A(u/\gamma, v)$$ for \(0 \le u \le \gamma\) and $$\mathbf{C}_{\gamma}(u,v) = (1-\gamma)\cdot\mathbf{C}_B((u-\gamma) \,/\,(1-\gamma), v)$$ for \(\gamma \le u \le 1\) and \(\gamma\) represents the gluing point in \(u\) (horizontal axis). The logic is simply the rescaling of \(\mathbf{C}_A\) to \([0,\gamma] \times [0,1]\) and \(\mathbf{C}_B\) to \([\gamma,1] \times [0,1]\). Copula gluing is potentially useful in circumstances for which regression is non-monotone.
glueCOP(u, v, para=NULL, ...)
Value(s) for the copula are returned.
Nonexceedance probability \(u\) in the \(X\) direction;
Nonexceedance probability \(v\) in the \(Y\) direction;
A special parameter list
(see Note) with a mandatory element of glue
parameter \(\gamma\); and
Additional arguments to pass to the copulas.
W.H. Asquith
Erdely, A., 2017, Copula-based piecewise regression (chap. 5) in Copulas and dependence models with applications---Contributions in honor of Roger B. Nelsen, eds. Flores, U.M., Amo Artero, E., Durante, F., Sánchez, J.F.: Springer, Cham, Switzerland, ISBN 978--3--319--64220--9, tools:::Rd_expr_doi("10.1007/978-3-319-64221-5").
COP
, breveCOP
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