Skew Student t-distribution maximum likelihood estimation

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.

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

Details

For the density function of the skew Student t-distribution see sstd.

References

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

Examples

mlsstd(precip)
#> Maximum likelihood estimates for the Skew Student-t model 
#>      mean         sd         nu         xi  
#> 3.470e+01  1.355e+01  1.868e+05  8.088e-01