mlinvburr {univariateML} | R Documentation |
Inverse Burr distribution maximum likelihood estimation
Description
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. |
Details
mlinvburr(x)
calls mlburr(1/x)
internally.
For the density function of the Inverse Burr distribution see Inverse 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 |
References
Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 20. Wiley, New York.
See Also
Inverse Burr for the Inverse Burr density.
Examples
mlburr(abalone$length)