MLEst: Maximum Likelihood Estimators of the two parameters of the...

In EWGoF: Goodness-of-Fit Tests for the Exponential and Two-Parameter Weibull Distributions

Description

This function computes the Maximum Likelihood Estimators (MLEs) of the shape and scale parameters of the Weibull distribution from an i.i.d sample x. It also gives the sample \hat{y} after using the logarithmic transformation (\hat{y}=(\hat{shape})\ln(x/\hat{scale}), where \hat{shape} and \hat{scale} are the estimated shape and scale parameters).

Usage

MLEst(x)

Arguments

x	a numeric vector of data values.

Details

The elements of the numeric vector should be positive. The support of the Weibull distribution is R+*.

Value

A list containing the following elements:

eta	the maximum likelihood estimator of the scale parameter of the Weibull distribution (\hat{scale}).
beta	the maximum likelihood estimator of the shape parameter of the Weibull distribution (\hat{shape}).
y	the pseudo-observations \hat{y} after using the logarithmic transformation and the MLEs.

Author(s)

Meryam KRIT

References

D'Agostino R.B. and Stephens M.A., Goodness-of-fit techniques, Marcel Dekker, 1986.

Krit M., Gaudoin O., Xie M. and Remy E., Simplified likelihood goodness-of-fit tests for the Weibull distribution, Communications in Statistics - Simulation and Computation.

Examples

x <- rweibull(50,2,3)

Est <- MLEst(x)

#Value of the maximum likelihood estimator of the scale parameter
Est$eta

#Value of the maximum likelihood estimator of the shape parameter
Est$beta

EWGoF documentation built on May 2, 2019, 6:09 a.m.