mlkumar: Kumaraswamy distribution maximum likelihood estimation

In JonasMoss/univariateML: Maximum Likelihood Estimation for Univariate Densities

Kumaraswamy distribution maximum likelihood estimation

Description

Uses Newton-Raphson to estimate the parameters of the Kumaraswamy distribution.

Usage

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

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	a0 is an optional starting value for the a parameter. rel.tol is the relative accuracy requested, defaults to .Machine$double.eps^0.25. iterlim is a positive integer specifying the maximum number of iterations to be performed before the program is terminated (defaults to 100).

Details

For the density function of the Kumaraswamy distribution see Kumaraswamy.

Value

mlkumar 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

Jones, M. C. "Kumaraswamy's distribution: A beta-type distribution with some tractability advantages." Statistical Methodology 6.1 (2009): 70-81.

Kumaraswamy, Ponnambalam. "A generalized probability density function for double-bounded random processes." Journal of Hydrology 46.1-2 (1980): 79-88.

See Also

Kumaraswamy for the Kumaraswamy density.

Examples

AIC(mlkumar(USArrests$Rape / 100))

JonasMoss/univariateML documentation built on Feb. 6, 2024, 2:21 p.m.