MGL.mle: Fitting bivariate MGL copula models for two continuous data

In lizhengxiao/rMGLReg: MGL copula regressions

Description

MGL.mle is used to fit bivariate copula regression models via maximum likelihood (ML) method for two continuous variables.

Usage

MGL.mle(
  U,
  copula = c("MGL", "MGL180", "MGL-EV", "MGL-EV180", "Gumbel", "Normal", "MGB2", "t"),
  hessian = TRUE,
  initpar,
  ...
)

Arguments

U	two-dimensional matrix with values in [0,1].
copula	copula 'MGL', 'MGL180', "MGL-EV", "MGL-EV180", "MGB2", "Normal" , "t".
hessian	Logical. Should a numerically differentiated Hessian matrix be returned?
initpar	Initial values for the parameters to be optimized over.
...	additional arguments, see nlm for more details.

Details

The estimation method is performed via nlm function.

copula:

• "MGB2" is multivariate GB2.

• "Normal" and "t" denote the Gaussian copula and Student-t copula respectively.

• "MGL" and "MGL-EV" denote the MGL and MGL-EV copula respectively.

• "MGL180" and "MGL-EV180" denote the survival MGL and survival MGL-EV copula respectively.

• "Gumbel" is Gumbel copula.

Value

A list containing the following components:

• loglike: the value of the estimated maximum of the loglikelihood function.

• copula: the name of the fitted copula. "MGL180" and "MGL-EV180" denote the survival MGL and MGL-EV copula respectively.

• estimates: the point at which the maximum value of the loglikelihood is obtained.

• se: the standard errors of the estimators.

• AIC, BIC: the goodness fit of the regression models.

• hessian: the hessian at the estimated maximum of the loglikelihood (if requested).

References

Zhang, F. Z. . "A generalized beta copula with applications in modeling multivariate long-tailed data." Insurance: Mathematics and Economics (2011).

Examples

  library(rMGLReg)
  Usim <- rcMGL.bivar(n = 500, pars = 0.5)
  m.MGL <- MGL.mle(Usim,
  copula  = "MGL",
  initpar = c(2))
  # estimation results
  m.MGL

lizhengxiao/rMGLReg documentation built on Jan. 2, 2022, 8:52 a.m.