mlstd: Student-t distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

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

Usage

mlstd(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 std.

Value

mlstd returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, nu 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

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 13. Wiley, New York.

See Also

std for the Student-t density.

Examples

mlstd(precip)

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