mlsnorm: Skew Normal distribution maximum likelihood estimation

Description Usage Arguments Details Value References See Also Examples

View source: R/mlsnorm.R

Description

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

Usage

1
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

1

Example output

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

univariateML documentation built on Aug. 6, 2020, 1:11 a.m.