mlpareto: Pareto distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of b is the minimum of x and the maximum likelihood estimate of a is 1/(mean(log(x)) - log(b)).

Usage

mlpareto(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

For the density function of the Pareto distribution see Pareto.

Value

mlpareto returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for a and b 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

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

See Also

Pareto for the Pareto density.

Examples

mlpareto(precip)

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