IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v37y2022i2p153-164n4.html
   My bibliography  Save this article

Estimation and Confidence Intervals of Modified Process Capability Index Using Robust Measure of Variability

Author

Listed:
  • Saha Mahendra

    (Department of Statistics, Central University of Rajasthan, Rajasthan, India)

  • Dey Sanku

    (Department of Statistics, St. Anthony’s College, Shillong, Meghalaya, India)

Abstract

The process capability index (PCI), denoted by 𝐼, is a well-known characteristic in quality control analysis. Using Gini’s mean difference, we construct a new PCI, IGI_{G} say, assuming the two-parameter Weibull distribution (WD). In order to estimate the proposed IGI_{G} when the process follows the WD, we use five classical methods of estimation and compare the performance of the obtained estimators with respect to their mean squared errors (MSEs) through a simulation study. Confidence intervals for the proposed PCI are constructed based on five bootstrap confidence intervals (BCIs) methods. Monte Carlo simulation study has been carried out to compare the performance of these five BCIs in terms of average widths and coverage probabilities. Finally, three real data sets from electronic and food industries are employed for illustrating the effectiveness of the proposed study. All these data sets show that the width of bias-corrected accelerated bootstrap interval is minimum among all other considered BCIs.

Suggested Citation

  • Saha Mahendra & Dey Sanku, 2022. "Estimation and Confidence Intervals of Modified Process Capability Index Using Robust Measure of Variability," Stochastics and Quality Control, De Gruyter, vol. 37(2), pages 153-164, December.
    • Handle: RePEc:bpj:ecqcon:v:37:y:2022:i:2:p:153-164:n:4
    DOI: 10.1515/eqc-2022-0014
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2022-0014
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/eqc-2022-0014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:ecqcon:v:37:y:2022:i:2:p:153-164:n:4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.