Interval Estimation for the Smaller-the-Better Type of Signal-to-Noise Ratio using Bootstrap Method.
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| Title: | Interval Estimation for the Smaller-the-Better Type of Signal-to-Noise Ratio using Bootstrap Method. |
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| Authors: | Chou, Chao-Yu1 (AUTHOR) choucy@pine.yuntech.edu.tw, Chen, Chung-Ho2 (AUTHOR), Liu, Hui-Rong3 (AUTHOR) |
| Source: | Quality Engineering. 2005, Vol. 17 Issue 1, p151-163. 13p. 11 Charts. |
| Subjects: | Statistical bootstrapping, Statistical sampling, Distribution (Probability theory), Confidence intervals, Statistical hypothesis testing, Signal-to-noise ratio, Simulation methods & models |
| Abstract: | The signal-to-noise ratio is an indicator, introduced by Taguchi, for evaluating the experimental data in robust design. Estimating the confidence interval of the signal-to-noise ratio is an important topic in data analysis of robust design. Calculating the confidence interval for a parameter usually needs the assumption about the underlying distributions. Bootstrapping is a nonparametric, but computer-intensive estimation method. In this article, we present the results of a simulation study on the behavior of three 95% bootstrap confidence intervals (i.e., SB, PB, and BCPB) for estimating the smaller-the-better signal-to-noise ratio when the data are from either a normal distribution or one of the Burr distributions. A detailed discussion of the simulation results is presented and some recommendations are given. [ABSTRACT FROM AUTHOR] |
| Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 15332244 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interval Estimation for the Smaller-the-Better Type of Signal-to-Noise Ratio using Bootstrap Method. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chou%2C+Chao-Yu%22">Chou, Chao-Yu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> choucy@pine.yuntech.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Chung-Ho%22">Chen, Chung-Ho</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Hui-Rong%22">Liu, Hui-Rong</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Quality+Engineering%22">Quality Engineering</searchLink>. 2005, Vol. 17 Issue 1, p151-163. 13p. 11 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+bootstrapping%22">Statistical bootstrapping</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+hypothesis+testing%22">Statistical hypothesis testing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The signal-to-noise ratio is an indicator, introduced by Taguchi, for evaluating the experimental data in robust design. Estimating the confidence interval of the signal-to-noise ratio is an important topic in data analysis of robust design. Calculating the confidence interval for a parameter usually needs the assumption about the underlying distributions. Bootstrapping is a nonparametric, but computer-intensive estimation method. In this article, we present the results of a simulation study on the behavior of three 95% bootstrap confidence intervals (i.e., SB, PB, and BCPB) for estimating the smaller-the-better signal-to-noise ratio when the data are from either a normal distribution or one of the Burr distributions. A detailed discussion of the simulation results is presented and some recommendations are given. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=15332244 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1081/QEN-200028999 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 151 Subjects: – SubjectFull: Statistical bootstrapping Type: general – SubjectFull: Statistical sampling Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Statistical hypothesis testing Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Simulation methods & models Type: general Titles: – TitleFull: Interval Estimation for the Smaller-the-Better Type of Signal-to-Noise Ratio using Bootstrap Method. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chou, Chao-Yu – PersonEntity: Name: NameFull: Chen, Chung-Ho – PersonEntity: Name: NameFull: Liu, Hui-Rong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: 2005 Type: published Y: 2005 Identifiers: – Type: issn-print Value: 08982112 Numbering: – Type: volume Value: 17 – Type: issue Value: 1 Titles: – TitleFull: Quality Engineering Type: main |
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