KIT | KIT-Bibliothek | Impressum | Datenschutz

On Testing the Adequacy of the Inverse Gaussian Distribution

Allison, James S.; Betsch, Steffen; Ebner, Bruno; Visagie, Jaco

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 L2-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


Verlagsausgabe §
DOI: 10.5445/IR/1000142847
Veröffentlicht am 08.02.2022
Originalveröffentlichung
DOI: 10.3390/math10030350
Scopus
Zitationen: 5
Web of Science
Zitationen: 5
Dimensions
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Stochastik (STOCH)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 2227-7390
KITopen-ID: 1000142847
Erschienen in Mathematics
Verlag MDPI
Band 10
Heft 3
Seiten Art.-Nr.: 350
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagwörter goodness-of-fit tests; inverse gaussian distribution; parametric bootstrap; stein-type characterization; warp-speed bootstrap
Nachgewiesen in Dimensions
Scopus
Web of Science
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page