Clayton.MixNormal.Markov.MLE: Maximum Likelihood Estimation using Newton-Raphson Method...

In Copula.Markov: Copula-Based Estimation and Statistical Process Control for Serially Correlated Time Series

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

Author(s)

Sun LH, Huang XW

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

data(DowJones)
Y=as.vector(DowJones$log_return)
Clayton.MixNormal.Markov.MLE(y=Y)

Copula.Markov documentation built on Nov. 29, 2021, 9:07 a.m.