Confidence Intervals for the Parameters of a Linguistic Random Variable

Kruse, Rudolf; Meyer, Klaus Dieter

doi:10.1007/978-3-642-46644-1_8

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…In Figure 9, the possibility 0 P is defined as Figure 9 shows the right-sided test for µ under fuzzy data. The probability associated with Z, based on Equations (2), (3), and (12), is defined as…”

Section: Single Normal Population With Known Population Variancementioning

confidence: 99%

“…Saade and Schwarzlander [10] discussed hypothesis testing for hybrid data, which is composed of fuzzy data and crisp data. Grzegorzewski [11] presented a corresponding fuzzy testing method by using fuzzy confidence intervals considered by Kruse and Meyer [12]. Filzmoser and Viertl [13] considered testing hypotheses with fuzzy data by the fuzzy p-value.…”

Section: Introductionmentioning

confidence: 99%

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Possibility Measure of Accepting Statistical Hypothesis

2020

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Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach.

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Section: Single Normal Population With Known Population Variancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Possibility Measure of Accepting Statistical Hypothesis

2020

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“…is unknown, therefore, it is desirable to use the statistical confidence interval. In this paper, we shall find the fuzzy reliability systems using fuzzy confidence interval attributed to Kruse and Meyer [12].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy System Reliability Analysis Based on Confidence Interval

Jamkhaneh

Nozari

2012

AMR

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This paper proposes a new method for analyzing the fuzzy system reliability of a parallel-series and series-parallel systems using fuzzy confidence interval, where the reliability of each component of each system is unknown. To compute system reliability, we are estimated reliability of each component of the systems using fuzzy statistical data with both tools appropriate for modeling fuzzy data and suitable statistical methodology to handle these data. Numerical examples are given to compute fuzzy reliability and its cut set and the calculating was performed by using programming in software R.

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Fuzzy Sets in the Evaluation of Reliability

Hryniewicz

2007

Studies in Computational Intelligence

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Confidence Intervals for the Parameters of a Linguistic Random Variable

Cited by 11 publications

References 6 publications

Possibility Measure of Accepting Statistical Hypothesis

Possibility Measure of Accepting Statistical Hypothesis

Fuzzy System Reliability Analysis Based on Confidence Interval

Fuzzy Sets in the Evaluation of Reliability

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