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<title>Vaisakh KM (Research Scholar)</title>
<link>http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/434</link>
<description/>
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<dc:date>2026-04-20T13:51:22Z</dc:date>
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<title>Goodness-of-fit tests for inverse Gaussian distribution in the presence and absence of censoring</title>
<link>http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/447</link>
<description>Goodness-of-fit tests for inverse Gaussian distribution in the presence and absence of censoring
Xavier, Thomas; Vaisakh, K. M.; Sreedevi, E. P
In this article, we use the fixed point characterization for inverse&#13;
Gaussian distribution to develop goodness of fit tests for the same.&#13;
First, we propose a test for inverse Gaussian distribution when the&#13;
data is complete. We then discuss, how the test procedure can&#13;
be modified to incorporate right-censored observations. We use&#13;
U-statistics theory to develop the test statistic. The large sample&#13;
behaviour of the proposed test statistics for both uncensored and&#13;
censored data are studied. We conduct extensive Monte Carlo simulation studies to validate the finite sample behaviour of the proposed&#13;
tests. The practical usefulness of the tests is illustrated using real data&#13;
sets. We also propose a new jackknife empirical likelihood ratio test&#13;
for the inverse Gaussian distribution with unit parameters.
</description>
<dc:date>2024-12-30T00:00:00Z</dc:date>
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