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Accuracy analysis of the percentile method for estimating non normal manufacturing quality
Journal article   Peer reviewed

Accuracy analysis of the percentile method for estimating non normal manufacturing quality

Chien-Wei Wu, W.L. Pearn, C.S. Chang and H.C. Chen
Communications in Statistics: Simulation and Computation, Vol.36(3), pp.657-697
05/2007

Abstract

Flexible capability indices Non normal distributions Relative bias Sample percentile estimator Simulation
Vnnman (1995) proposed a superstructure Cp (u, v) of capability indices for processes with normal distributions, which include Cp, Cpk, Cpm, and Cpmk as special cases. Pearn and Chen (1997) considered a generalization of Cp (u, v), called CNp (u, v), to cover processes with non normal distributions. Pearn and Chen (1997) also proposed a sample percentile estimator for the generalization CNp (u, v). In this article, we investigate the performance of the sample percentile estimator. We perform some simulation study, which covers the normal distribution and various non normal distributions including the uniform distribution, chi-square distribution, student's t distributions, F distribution, beta distribution, gamma distribution, Weibull distribution, lognormal distribution, triangular distribution, and Laplace distribution, with selected parameter values. Extensive simulation results, comparisons, and analysis are provided.

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