delta.test {LambertW} | R Documentation |
Given the simulated critical values for the GMM method and for delta=0 (see Goerg (2009)), this function performs a test on the significance an estimated delta.
delta.test(object)
object |
object of class LWest |
If the data is truly Gaussian – hence true delta = 0 – then
frac{widehat{delta} sqrt{N}}{0.4} sim N(0,1),
where N is the sample size, and widehat{delta} is the estimated delta via GMM.
A list with class "htest" containing the following components:
statistic |
the value of the test statistic. |
p.value |
the p-value for the test. |
method |
the character string "Simulation based GMM significanc test (Asymptotic Gaussian)" |
data.name |
a character string giving the name(s) of the data. |
Georg M. Goerg
Goerg, G.M. (2009). “Lambert W Random Variables - A new class of skewed distribution functions”. Unpublished
set.seed(1) y=rnorm(1000) fit=IGMM(y) delta.test(fit) # not-significant because data is truly Gaussian data(AA) attach(AA) X=AA[AA$sex=="f",] y=X$bmi fit=IGMM(y) delta.test(fit) ### Significant delta as the data is highly skewed to the right