Skip to main content
padlock icon - secure page this page is secure

Distributional and Inferential Properties of the Process Loss Indices

Buy Article:

$61.00 + tax (Refund Policy)

Johnson (1992) developed the process loss index Le, which is defined as the ratio of the expected quadratic loss to the square of half specification width. Tsui (1997) expressed the index Le as Le=Lpe+Lot, which provides an uncontaminated separation between information concerning the potential relative expected loss (Lpe) and the relative off-target squared (Lot), as the ratio of the process variance and the square of the half specification width, and the square of the ratio of the deviation of mean from the target and the half specification width, respectively. In this paper, we consider these three loss function indices, and investigate the statistical properties of their natural estimators. For the three indices, we obtain their UMVUEs and MLEs, and compare the reliability of the two estimators based on the relative mean squared errors. In addition, we construct 90%, 95%, and 99% upper confidence limits, and the maximum values of L^e for which the process is capable, 90%, 95%, and 99% of the time. The results obtained in this paper are useful to the practitioners in choosing good estimators and making reliable decisions on judging process capability.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: MLE, potential relative expected loss; UMVUE; relative expected loss; relative mean squared error; relative off-target squared

Document Type: Research Article

Affiliations: 1: Department of Industrial Engineering & Management, National Chiao Tung University, Taiwan 2: Department of Industrial Engineering & Management, Ching Yun University, Taiwan 3: Department of Business Administration, Feng Chia University, Taiwan

Publication date: November 1, 2004

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more