cbCopula contructor
cbCopula(x, m = rep(nrow(x), ncol(x)), pseudo = FALSE)the data to be used
checkerboard parameters
Boolean, defaults to FALSE. Set to TRUE if you are already
providing pseudo data into the x argument.
An instance of the cbCopula S4 class. The object represent the fitted copula and can be used through several methods to query classical (r/d/p/v)Copula methods, etc.
The cbCopula class computes a checkerboard copula with a given checkerboard parameter \(m\), as described by A. Cuberos, E. Masiello and V. Maume-Deschamps (2019). Assymptotics for this model are given by C. Genest, J. Neslehova and R. bruno (2017). The construction of this copula model is as follows :
Start from a dataset with \(n\) i.i.d observation of a \(d\)-dimensional copula (or pseudo-observations), and a checkerboard parameter \(m\),dividing \(n\).
Consider the ensemble of multi-indexes \(I = \{i = (i_1,..,i_d) \subset \{1,...,m \}^d\}\) which indexes the boxes :
$$B_{i} = \left]\frac{i-1}{m},\frac{i}{m}\right]$$
Let now \(\lambda\) be the dimension-unspecific lebesgue measure on any power of \(R\), that is :
$$\forall d \in N, \forall x,y \in R^p, \lambda(\left(x,y\right)) = \prod\limits_{p=1}^{d} (y_i - x_i)$$
Let furthermore \(\mu\) and \(\hat{\mu}\) be respectively the true copula measure of the sample at hand and the classical Deheuvels empirical copula, that is :
For \(n\) i.i.d observation of the copula of dimension \(d\), let \(\forall i \in \{1,...,d\}, \, R_i^1,...,R_i^d\) be the marginal ranks for the variable \(i\).
\(\forall x \in I^d\) let \(\hat{\mu}((0,x)) = \frac{1}{n} \sum\limits_{k=1}^n I_{R_1^k\le x_1,...,R_d^k\le x_d}\)
The checkerboard copula, \(C\), and the empirical checkerboard copula, \(\hat{C}\), are then defined by the following :
$$\forall x \in (0,1)^d, C(x) = \sum\limits_{i\in I} {m^d \mu(B_{i}) \lambda((0,x)\cap B_{i})}$$
Where \(m^d = \lambda(B_{i})\).
This copula is a special form of patchwork copulas, see F. Durante, J. Fern<U+00E1>ndez S<U+00E1>nchez and C. Sempi (2013) and F. Durante, J. Fern<U+00E1>ndez S<U+00E1>nchez, J. Quesada-Molina and M. Ubeda-Flores (2015). The estimator has the good property of always being a copula.
The checkerboard copula is a kind of patchwork copula that only uses independent copula as fill-in, only where there are values on the empirical data provided. To create such a copula, you should provide data and checkerboard parameters (depending on the dimension of the data).
cuberos2019cort
genest2017cort
durante2013cort
durante2015cort