LRI.test: GoF tests based on the Laplace transform, the mean residual...

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

Description

Computes the Weibull GoF tests based on the Laplace transform: Baringhaus-Henze (BH) and Henze (He). The test statistic of Klar (Kl) is based on the integrated distribution function. Two tests are based on the mean residual life (BHC, BHK).

Usage

LRI.test(x, type = "BH", a = 1, nsim = 200)

Arguments

x	a numeric vector of data values.
type	the type of the test statistic used. "BH" is the default used test of Baringhaus-Henze,"He" for Henze, "Kl" for Klar, "BHC" and "BHK" for the tests based on the integrated distribution function.
a	parameter value to be adjusted for the test statistics.
nsim	an integer specifying the number of replicates used in Monte Carlo.

Value

An object of class htest.

Author(s)

Meryam KRIT

References

Baringhaus L. and Henze N., Tests of fit for exponentiality based on a characterization via the mean residual life function, Statistical Papers, 41, 225-236, 2000.

Baringhaus L. and Henze N., A class of consistent tests for exponentiality based on the empirical Laplace transform, Annals of the Institute of Statistical Mathematics, 43, 551-564, 1991.

Henze N., A new flexible class of omnibus tests for exponentiality, Communications in Statistics, Theory and Methods, 22, 115-133, 1993.

Klar B., Goodness-of-fit tests for the exponential and normal distribution based on the integrated distribution function, Annals of the Institute of Statistical Mathematics, 53, 338-353, 2001.

Examples

x <- rlnorm(50,0.3)

#Apply the Baringhaus-Henze test
LRI.test(x,type="BH")

# Apply the test of Henze
LRI.test(x,type="He")


# Apply the test of Klar
LRI.test(x,type="Kl")


# Apply the test of Barighaus based on the integrated distribution function
LRI.test(x,type="BHC")

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