mlsstd: Skew Student t-distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

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

Usage

mlsstd(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 skew Student t-distribution see sstd.

Value

mlsstd returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, nu, xi 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

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

See Also

sstd for the Skew Student t-density.

Examples

mlsstd(precip)

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