Linear Hypothesis Testing Based on Unbiased Fuzzy Estimators and Fuzzy Significance Level

Chapter
First Online:
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 343)

Abstract

A wide variety of applied problems of statistical hypothesis testing can be treated under a general setup of the linear models which includes analysis of variance. In this study, a new method is presented to test linear hypothesis using a fuzzy test statistic produced by a set of confidence intervals with non-equal tails. Also, a fuzzy significance level is used to evaluate the linear hypothesis. The method can be used to improve linear hypothesis testing when there is a sensitively in accepting or rejecting the null hypothesis. Also, as a simple case of linear hypothesis testing, one-way analysis of variance based on fuzzy test statistic and fuzzy significance level is investigated. Numerical examples are provided for illustration.

Keywords

Analysis of variance Confidence interval Fuzzy critical value Fuzzy test statistic Fuzzy significance level Linear hypothesis Linear model 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Statistics, Faculty of Mathematics and Computer SciencesShahid Bahonar University of KermanKermanIran

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