mlged: Generalized Error distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

Joint maximum likelihood estimation as implemented by fGarch::gedFit.

Usage

mlged(x, na.rm = FALSE, ...)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	currently affects nothing.

Details

For the density function of the Student t-distribution see ged.

Value

mlged returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, nu and the following attributes:

model	The name of the model.
density	The density associated with the estimates.
logLik	The loglikelihood at the maximum.
support	The support of the density.
n	The number of observations.
call	The call as captured my match.call

References

Nelson D.B. (1991); Conditional Heteroscedasticity in Asset Returns: A New Approach, Econometrica, 59, 347–370.

Fernandez C., Steel M.F.J. (2000); On Bayesian Modelling of Fat Tails and Skewness, Preprint.

See Also

ged for the Student t-density.

Examples

mlged(precip)

Example output

Maximum likelihood estimates for the Generalized Error model 
  mean      sd      nu  
35.330  13.626   1.772  

univariateML documentation built on Jan. 25, 2022, 5:09 p.m.