mlinvweibull: Inverse Weibull distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of shape and rate are calculated by calling mlweibull on the transformed data.

Usage

mlinvweibull(x, na.rm = FALSE, ...)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	passed to mlweibull.

Details

For the density function of the log normal distribution see InverseWeibull.

Value

mlinvweibull returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape and rate 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

Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

Dutang, C., Goulet, V., & Pigeon, M. (2008). actuar: An R package for actuarial science. Journal of Statistical Software, 25(7), 1-37.

See Also

InverseWeibull for the Inverse Weibull density.

Examples

mlinvweibull(precip)

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