mlsnorm: Skew Normal distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

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.

Details

For the density function of the Student t distribution see dsnorm.

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

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)

Example output

Maximum likelihood estimates for the Skew Normal model 
   mean       sd       xi  
34.6957  13.5471   0.8088  

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