test.ABEV2 {gofIG} | R Documentation |
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.
test.ABEV2(data, a = 10, meth = "MME", B = 500)
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. |
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.
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.
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
test.ABEV2(rmutil::rinvgauss(20,2,1),B=100)