Copula.Markov (version 2.9)

Clayton.MixNormal.Markov.MLE: Maximum Likelihood Estimation using Newton-Raphson Method Under the Clayton Copula and the Mix-Normal distribution

Description

The maximum likelihood estimates are produced. The dependence model follows the Clayton copula and the marginal distribution follows the Mix-Normal distribution.

Usage

Clayton.MixNormal.Markov.MLE(y)

Arguments

y

vector of datasets

Value

alpha

estimate, SE, and 95 percent CI

mu1

estimate, SE, and 95 percent CI

mu2

estimate, SE, and 95 percent CI

sigma1

estimate, SE, and 95 percent CI

sigma2

estimate, SE, and 95 percent CI

p

estimate, SE, and 95 percent CI

Gradient

gradients (must be zero)

Hessian

Hessian matrix

Eigenvalue_Hessian

Eigenvalues for the Hessian matrix

log.likelihood

Log-likelihood value for the estimation

References

Lin WC, Emura T, Sun LH (2021), Estimation under copula-based Markov normal mixture models for serially correlated data, Communications in Statistics - Simulation and Computation, 50(12):4483-515

Examples

# NOT RUN {
data(DowJones)
Y=as.vector(DowJones$log_return)
Clayton.MixNormal.Markov.MLE(y=Y)
# }