New weighted $L^2$-type tests for the inverse Gaussian distribution.

摘要: We propose a new class of goodness-of-fit tests for the inverse Gaussian distribution. The proposed are weighted $L^2$-type depending on tuning parameter. develop asymptotic theory under null hypothesis and broad alternative distributions. These results used to show that parametric bootstrap procedure, which we employ implement test, is asymptotically valid whole test procedure consistent. A comparative simulation study finite sample sizes shows competitive classical recent tests, outperforming these other methods almost uniformly over large set use newly illustrated with two observed data sets.