# 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

 `1` ```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.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 Sept. 15, 2017, 5:03 p.m.