nfcm_mle: Maximum likelihood estimator of Spline Based Nonlinear Factor...

In samorso/nfcm: Spline Based Nonlinear Factor Copula Model

Maximum likelihood estimator of Spline Based Nonlinear Factor Copula Model

Description

Minimize the negative log-likelihood for a bivariate model. The copula models assume positive quadrant dependent variables. If one has N-dimensional variable, the variable is transformed to a 2-dimensional matrix (see 'Details').

Usage

nfcm_mle(
  lambda,
  w,
  type = "b",
  splines_control = splines.control(),
  mle_control = mle.control()
)

Arguments

lambda	vector of starting values for spline coefficients (\Lambda vectorized).
w	uniform(0,1) n\times N matrix of observations.
type	specify spline basis, either "b" (default), "c", "i" or "m";
splines_control	control (see splines.control).
mle_control	control to pass to the optimization routine (see 'Details').

Value

An object from the optimization routine (see 'Details')

See Also

slsqp

samorso/nfcm documentation built on Oct. 13, 2024, 9:35 p.m.