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

Chien Wei Wu*, W.l. Pearn, C. S. Chang, H. C. Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Pages (from-to)657-697
Number of pages41
JournalCommunications in Statistics: Simulation and Computation
Issue number3
StatePublished - 1 May 2007


  • Flexible capability indices
  • Non normal distributions
  • Relative bias
  • Sample percentile estimator
  • Simulation

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