mBICV {rvinecopulib} | R Documentation |
Modified vine copula Bayesian information criterion (mBICv)
Description
Calculates the modified vine copula Bayesian information criterion.
Usage
mBICV(object, psi0 = 0.9, newdata = NULL)
Arguments
object |
a fitted |
psi0 |
baseline prior probability of a non-independence copula. |
newdata |
optional; a new data set. |
Details
The modified vine copula Bayesian information criterion (mBICv) is defined as
BIC = -2 loglik + \nu log(n) - 2 \sum_{t=1}^{d - 1} (q_t log(\psi_0^t)
- (d - t - q_t) log(1 - \psi_0^t))
where \mathrm{loglik}
is the log-likelihood and \nu
is the
(effective) number of parameters of the model, t
is the tree level
\psi_0
is the prior probability of having a non-independence copula and
q_t
is the number of non-independence copulas in tree t
. The
mBICv is a consistent model selection criterion for parametric sparse vine
copula models.
References
Nagler, T., Bumann, C., Czado, C. (2019). Model selection for sparse high-dimensional vine copulas with application to portfolio risk. Journal of Multivariate Analysis, in press (https://arxiv.org/pdf/1801.09739.pdf)
Examples
u <- sapply(1:5, function(i) runif(50))
fit <- vinecop(u, family = "par", keep_data = TRUE)
mBICV(fit, 0.9) # with a 0.9 prior probability of a non-independence copula
mBICV(fit, 0.1) # with a 0.1 prior probability of a non-independence copula