On the sampling distributions of the estimated process loss indices with asymmetric tolerances

Y. C. Chang*, W.l. Pearn, Chien Wei Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Pearn et al. (2006a) proposed a new generalization of expected loss index L''e to handle processes with both symmetric and asymmetric tolerances. Putting the loss in relative terms, a user needs only to specify the target and the distance from the target at which the product would have zero worth to quantify the performance of a process. The expected loss index L''e may be expressed as L''e=L''ot+L''pe, which provides an uncontaminated separation between information concerning the process accuracy and the process precision. In order to apply the theory of testing statistical hypothesis to test whether a process is capable or not under normality assumption, in this paper we first derive explicit form for the cumulative distribution function and the probability density function of the natural estimator of the three indices L''ot, L''pe, and L''e. We have proved that the sampling distributions of [image omitted] and [image omitted] may be expressed as the chi-square distribution and the normal distribution, respectively. And the distribution of [image omitted] can be described in terms of a mixture of the chi-square distribution and the normal distribution. Then, we develop a decision-making rule based on the estimated index [image omitted]. Finally, an example of testing L''e is also presented for illustrative purpose.

Original languageEnglish
Pages (from-to)1153-1170
Number of pages18
JournalCommunications in Statistics: Simulation and Computation
Volume36
Issue number6
DOIs
StatePublished - 1 Nov 2007

Keywords

  • Asymmetric tolerances
  • Decision-making rule
  • Process capability indices
  • Process loss indices
  • Sampling distributions

Fingerprint Dive into the research topics of 'On the sampling distributions of the estimated process loss indices with asymmetric tolerances'. Together they form a unique fingerprint.

Cite this