The maximum likelihood estimate of `shape`

and `rate`

are calculated
by calling `mlweibull`

on the transformed data.

## Arguments

- x
a (non-empty) numeric vector of data values.

- na.rm
logical. Should missing values be removed?

- ...
passed to

`mlweibull`

.

## 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`

## Details

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

## 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.