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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 08982112 |
| DOI: | 10.1081/QEN-200028999 |
