Interval estimation for the larger-the-better type of signal-to-nose ratio using bootstrap method

Abstract

The signal-to-noise ratio is an indicator, introduced by Taguchi, for evaluating the experimental data in robust design. Constructing 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 distribution. Bootstrapping is a nonparametric, but computer intensive, estimation method. In this paper 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 larger-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.

Keywords:

• Bootstrap confidence interval
• signal-to-noise ratio
• simulation
• the Burr distribution