Inverse Burr distribution maximum likelihood estimation

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

Usage

mlinvburr(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

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

Details

mlinvburr(x) calls mlburr(1/x) internally.

For the density function of the Inverse Burr distribution see Inverse Burr.

References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 20. Wiley, New York.

Examples

mlburr(abalone$length)
#> Maximum likelihood estimates for the Burr model 
#> shape1  shape2  
#> 22.149   5.452