test.ABEV2 {gofIG}R Documentation

The second Allison-Betsch-Ebner-Visagie goodness-of-fit test for the inverse Gaussian family

Description

This function computes the goodness-of-fit test for the inverse Gaussian family due to Allison et al. (2019). Two different estimation procedures are implemented, namely the method of moment and the maximum likelihood method.

Usage

test.ABEV2(data, a = 10, meth = "MME", B = 500)

Arguments

data

a vector of positive numbers.

a

positive tuning parameter.

meth

method of estimation used. Possible values are 'MME' for moment estimation and 'MLE' for maximum likelihood estimation.

B

number of bootstrap iterations used to obtain p value.

Details

The test is of weighted L^2 type and uses a characterization of the distribution function of the inverse Gaussian distribution. The p value is obtained by a parametric bootstrap procedure.

Value

a list containing the value of the name of the test statistic, the used tuning parameter, the parameter estimation method, the value of the test statistic, the bootstrap p value, the values of the estimators, and the number of bootstrap iterations:

$Test

the name of the used test statistic.

$parameter

the value of the tuning parameter.

$est.method

the estimation method used.

$T.value

the value of the test statistic.

$p.value

the approximated p value.

$par.est

the estimated parameters.

$boot.run

number of bootstrap iterations.

References

Allison, J.S., Betsch, S., Ebner, B., Visagie, I.J.H. (2019) "New weighted L^2-type tests for the inverse Gaussian distribution", arXiv:1910.14119. LINK

Examples

test.ABEV2(rmutil::rinvgauss(20,2,1),B=100)


[Package gofIG version 1.0 Index]