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Fuzzy Statistical Decision-Making Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

Authors : Alireza Jiryaei, Mashaallah Mashinchi

Published in: Fuzzy Statistical Decision-Making

Publisher: Springer International Publishing

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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.

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previous chapter Fuzzy EWMA and Fuzzy CUSUM Control Charts
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Metadata
Title
Linear Hypothesis Testing Based on Unbiased Fuzzy Estimators and Fuzzy Significance Level
Authors
Alireza Jiryaei
Mashaallah Mashinchi
Copyright Year
2016
Book
Fuzzy Statistical Decision-Making

Print ISBN: 978-3-319-39012-3

Electronic ISBN: 978-3-319-39014-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39014-7_16

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