Estimating general variable acceptance sampling plans by bootstrap methods

Herwig Friedl and Erwin Stampfer

Journal of Applied Statistics, 2002, vol. 29, issue 8, 1205-1217

Abstract: We consider variable acceptance sampling plans that control the lot or process fraction defective, where a specification limit defines acceptable quality. The problem is to find a sampling plan that fulfils some conditions, usually on the operation characteristic. Its calculation heavily depends on distributional properties that, in practice, might be doubtful. If prior data are already available, we propose to estimate the sampling plan by means of bootstrap methods. The bias and standard error of the estimated plan can be assessed easily by Monte Carlo approximation to the respective bootstrap moments. This resampling approach does not require strong assumptions and, furthermore, is a flexible method that can be extended to any statistic that might be informative for the fraction defective in a lot.

Date: 2002
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0266476022000011274 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:29:y:2002:i:8:p:1205-1217

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/0266476022000011274

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().