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Interval Estimation for the Smaller-the-Better Type of Signal-to-Noise Ratio Using Bootstrap Method

Summary: [This abstract is based on the authors' abstract.]Estimating the confidence interval of the signal-to-noise ratio is critical for evaluating the data in robust design. Calculating the confidence interval for a parameter usually requires assumptions about the underlying distribution. Bootstrapping is a nonparametric method of obtaining confidence intervals without any assumption of underlying distribution that can be successfully applied when sample sizes are small. A simulation study examines the behavior of three 95 percent bootstrap confidence intervals for estimating the smaller-the-better signal-to-noise ratio when data are from either normal distribution or one of the Burr distributions.

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  • Topics: Sampling
  • Keywords: Bootstrap methods,Confidence intervals,Simulations,Sample size,Signal-to-noise ratio,Nonnormal distribution curve
  • Author: Chou, Chao-Yu; Chen, Chung-Ho; Liu, Hui-Rong
  • Journal: Quality Engineering