On Testing the Adequacy of the Inverse Gaussian Distribution

James S. Allison, Steffen Betsch, Bruno Ebner and Jaco Visagie
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James S. Allison: School of Mathematical and Statistical Sciences, North-West University, Potchefstroom 2531, South Africa
Steffen Betsch: Institute of Stochastics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Bruno Ebner: Institute of Stochastics, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Jaco Visagie: School of Mathematical and Statistical Sciences, North-West University, Potchefstroom 2531, South Africa

Mathematics, 2022, vol. 10, issue 3, 1-18

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

Keywords: goodness-of-fit tests; inverse gaussian distribution; parametric bootstrap; stein-type characterization; warp-speed bootstrap (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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