Skew Normal distribution maximum likelihood estimation

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

Usage

mlsnorm(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

mlsnorm returns an object of class

univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, 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 Student t distribution see dsnorm.

References

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

See also

dsnorm for the Student-t density.

Examples

mlsnorm(precip)
#> Maximum likelihood estimates for the Skew Normal model 
#>    mean       sd       xi  
#> 34.6957  13.5471   0.8088