mlburr: Burr distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Burr distribution maximum likelihood estimation

Description

The maximum likelihood estimator fails to exist when the data contains no values strictly smaller than 1. Then the likelihood converges to the likelihood of the Pareto distribution in this case.

Usage

mlburr(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

This function estimates the only the shape parameters of the Burr distribution. The shape is set to 1.

For the density function of the Burr distribution see Burr.

Value

mlburr returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape1 and shape2 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 20. Wiley, New York.

See Also

Burr for the Burr density.

Examples

mlburr(abalone$length)

univariateML documentation built on April 3, 2025, 11:09 p.m.