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 |
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)