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An Introduction to Fuzzy Testing of Multialternative Hypotheses for Group of Samples with the Single Parameter: Through the Fuzzy Confidence Interval of Region of Acceptance

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  • Manikandan Harikrishnan
  • Jeyabharathi Sundarrajan
  • Muthuraj Rengasamy

Abstract

Classical statistics and many data mining methods rely on “statistical significance” as a sole criterion for evaluating alternative hypotheses. It is very useful to find out the significant difference existing between the samples as well as the population or between two samples. But in this paper, the researchers try to apply the concepts of fuzzy group testing of hypothesis problem between multi group of samples of same size or different, through comparing the parameters like mean, standard deviation, and so forth. Hence we can compare multigroups such that they have the significant difference in their mean or standard deviation or other parameters through the fuzzy group testing of multihypotheses. The authors introduced and investigated the concepts very first time through fuzzy analysis that can decide which group(s) or samples can be taken for further investigation and either H0 is rejected or accepted and hence the next discussion provides the properties of group of samples which may result in the optimized solution for the problem.

Suggested Citation

  • Manikandan Harikrishnan & Jeyabharathi Sundarrajan & Muthuraj Rengasamy, 2015. "An Introduction to Fuzzy Testing of Multialternative Hypotheses for Group of Samples with the Single Parameter: Through the Fuzzy Confidence Interval of Region of Acceptance," Journal of Applied Mathematics, John Wiley & Sons, vol. 2015(1).
    • Handle: RePEc:wly:jnljam:v:2015:y:2015:i:1:n:365304
    DOI: 10.1155/2015/365304
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    References listed on IDEAS

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    1. Bernhard Arnold, 1996. "An approach to fuzzy hypothesis testing," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 44(1), pages 119-126, December.
    2. Hamzeh Torabi & Javad Behboodian & S. Taheri, 2006. "Neyman–Pearson Lemma for Fuzzy Hypotheses Testing with Vague Data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 64(3), pages 289-304, December.
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