TY - JOUR

T1 - On the sampling distributions of the estimated process loss indices with asymmetric tolerances

AU - Chang, Y. C.

AU - Pearn, W.l.

AU - Wu, Chien Wei

PY - 2007/11/1

Y1 - 2007/11/1

N2 - 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.

AB - 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.

KW - Asymmetric tolerances

KW - Decision-making rule

KW - Process capability indices

KW - Process loss indices

KW - Sampling distributions

UR - http://www.scopus.com/inward/record.url?scp=36549008252&partnerID=8YFLogxK

U2 - 10.1080/03610910701569168

DO - 10.1080/03610910701569168

M3 - Article

AN - SCOPUS:36549008252

VL - 36

SP - 1153

EP - 1170

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

SN - 0361-0918

IS - 6

ER -