Hypothesis testing for the mean of inverse Gaussian distribution using α-cuts

Hypothesis testing for the mean of inverse Gaussian distribution using α-cuts

In this study, we modify the method proposed by Buckley to testing statistical hypothesis for the mean of an inverse Gaussian distribution. In order to obtain fuzzy test statistic, we use confidence intervals by the help of α -cuts. Then the method is applied to test the hypothesis for the mean of i...

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Veröffentlicht in:Soft computing (Berlin, Germany) Germany), 2015, Vol.19 (1), p.113-119
Hauptverfasser: İçen, Duygu, Bacanlı, Sevil
Format: Artikel
Sprache:eng
Schlagworte:
Artificial Intelligence
Binomial distribution
Computational Intelligence
Confidence intervals
Control
Engineering
Fuzzy sets
Hypotheses
Hypothesis testing
Inverse Gaussian probability distribution
Mathematical Logic and Foundations
Mean
Mechatronics
Methodologies and Application
Normal distribution
Parameter estimation
Random variables
Robotics
Statistical analysis
Test procedures
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Beschreibung
Zusammenfassung:In this study, we modify the method proposed by Buckley to testing statistical hypothesis for the mean of an inverse Gaussian distribution. In order to obtain fuzzy test statistic, we use confidence intervals by the help of α -cuts. Then the method is applied to test the hypothesis for the mean of inverse Gaussian distribution when the scale parameter is known. Also a comparison is made between the fuzzy and non-fuzzy test procedure for the inverse Gaussian distribution.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-014-1235-7