View source: R/bCond.estimation.R
bCond.pobs | R Documentation |
Let A_1, ..., A_p
be p
events forming a partition of
a probability space and X_1, ..., X_d
be d
random variables.
Assume that we observe n
i.i.d. replications of (X_1, ..., X_d)
,
and that for each i=1, ..., d
,
V_{i,j|A} = F_{X_j | A_k}(X_{i,j} | A_k),
we also know which of the A_k
was realized.
This function computes the pseudo-observations
where k
is such that the event A_k
is realized for the i
-th observation.
bCond.pobs(X, partition)
X |
matrix of size |
partition |
matrix of size |
a matrix of size n * d
containing the conditional pseudo-observations V_{i,j|A}
.
Derumigny, A., & Fermanian, J. D. (2017). About tests of the “simplifying” assumption for conditional copulas. Dependence Modeling, 5(1), 154-197. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1515/demo-2017-0011")}
Derumigny, A., & Fermanian, J. D. (2022) Conditional empirical copula processes and generalized dependence measures Electronic Journal of Statistics, 16(2), 5692-5719. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/22-EJS2075")}
bCond.estParamCopula
for the estimation
of a (conditional) parametric copula model in this framework.
bCond.treeCKT
that provides a binary tree
based on conditional Kendall's tau
and that can be used to derive relevant conditioning events.
n = 800
Z = stats::runif(n = n)
CKT = 0.2 * as.numeric(Z <= 0.3) +
0.5 * as.numeric(Z > 0.3 & Z <= 0.5) +
- 0.8 * as.numeric(Z > 0.5)
simCopula = VineCopula::BiCopSim(N = n,
par = VineCopula::BiCopTau2Par(CKT, family = 1), family = 1)
X1 = simCopula[,1]
X2 = simCopula[,2]
partition = cbind(Z <= 0.3, Z > 0.3 & Z <= 0.5, Z > 0.5)
condPseudoObs = bCond.pobs(X = cbind(X1, X2),
partition = partition)
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