mlsstd {univariateML}

Skew Student t-distribution maximum likelihood estimation

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)