View source: R/bCond.simpA.param.R
bCond.simpA.param | R Documentation |
Test of the assumption that a conditional copulas does not vary through a list of discrete conditioning events
bCond.simpA.param(
X1,
X2,
partition,
family,
testStat = "T2c_tau",
typeBoot = "boot.NP",
nBootstrap = 100
)
X1 |
vector of |
X2 |
vector of |
partition |
matrix of size |
family |
family of parametric copulas used |
testStat |
test statistic used. Possible choices are
|
typeBoot |
type of bootstrap used |
nBootstrap |
number of bootstrap replications |
a list containing
true_stat
:
the value of the test statistic computed on the whole sample
vect_statB
:
a vector of length nBootstrap
containing the bootstrapped
test statistics.
p_val
: the p-value of the test.
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.simpA.CKT
for a test of the simplifying assumption
that all these conditional copulas are equal,
based on the equality of conditional Kendall's tau
(i.e. without any parametric assumption).
Tests of the simplifying assumption for conditional copulas with a continuous conditioning variable:
simpA.NP
in a nonparametric setting
simpA.param
in a (semi)parametric setting,
where the conditional copula belongs to a parametric family,
but the conditional margins are estimated arbitrarily through
kernel smoothing
simpA.kendallReg
: test based on the constancy of
conditional Kendall's tau
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) +
+ 0.3 * as.numeric(Z > 0.5)
family = 3
simCopula = VineCopula::BiCopSim(N = n,
par = VineCopula::BiCopTau2Par(CKT, family = family), family = family)
X1 = simCopula[,1]
X2 = simCopula[,2]
partition = cbind(Z <= 0.3, Z > 0.3 & Z <= 0.5, Z > 0.5)
result = bCond.simpA.param(X1 = X1, X2 = X2, testStat = "T2c_tau",
partition = partition, family = family, typeBoot = "boot.paramInd")
print(result$p_val)
n = 800
Z = stats::runif(n = n)
CKT = 0.1
family = 3
simCopula = VineCopula::BiCopSim(N = n,
par = VineCopula::BiCopTau2Par(CKT, family = family), family = family)
X1 = simCopula[,1]
X2 = simCopula[,2]
partition = cbind(Z <= 0.3, Z > 0.3 & Z <= 0.5, Z > 0.5)
result = bCond.simpA.param(X1 = X1, X2 = X2,
partition = partition, family = family, typeBoot = "boot.NP")
print(result$p_val)
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