Inverse Gaussian (Wald) maximum likelihood estimation

The maximum likelihood estimate of mean is the empirical mean and the maximum likelihood estimate of 1/shape is the difference between the mean of reciprocals and the reciprocal of the mean.

Usage

mlinvgauss(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

mlinvgauss returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for mean and shape 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 Inverse Gamma distribution see InverseGaussian.

References

Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995) Continuous Univariate Distributions, Volume 1, Chapter 15. Wiley, New York.

Examples

mlinvgauss(precip)
#> Maximum likelihood estimates for the Inverse Gaussian model 
#>   mean   shape  
#>  34.89  107.48