IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v34y2007i2p203-216.html
   My bibliography  Save this article

A Non-parametric Method for Defining a Global Preference Ranking of Industrial Products

Author

Listed:
  • L. Corain
  • L. Salmaso

Abstract

Although experimentation is a crucial stage in the process of research and development of industrial products, no satisfactory procedure is available to deal with the common but rather important industrial problem of defining a preference ranking among all the studied product prototypes on the basis of performances. In this paper we propose a two-stage non-parametric procedure in which we firstly perform a set of C-sample testing procedures, followed by multiple comparisons, in this way evaluating a set of partial preference rankings, and secondly synthesise the partial rankings by combining them into a global ranking that provides a general product preference rule. The proposed method is particularly useful in the context of industrial experimentation and offers several advantages such as effectiveness, high flexibility and practical adherence to real problems where preference ranking is a natural goal.

Suggested Citation

  • L. Corain & L. Salmaso, 2007. "A Non-parametric Method for Defining a Global Preference Ranking of Industrial Products," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(2), pages 203-216.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:2:p:203-216
    DOI: 10.1080/02664760600995122
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600995122
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760600995122?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.

    References listed on IDEAS

    as
    1. Luigi Salmaso & Aldo Solari, 2005. "Multiple aspect testing for case-control designs," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 62(2), pages 331-340, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nestoras Karathanasis & Ioannis Tsamardinos & Vincenzo Lagani, 2016. "omicsNPC: Applying the Non-Parametric Combination Methodology to the Integrative Analysis of Heterogeneous Omics Data," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-23, November.
    2. Tang, Min & Slud, Eric V. & Pfeiffer, Ruth M., 2014. "Goodness of fit tests for linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 176-193.
    3. Dominika Polko-Zając, 2019. "On Permutation Location–Scale Tests," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 153-166, December.
    4. Polko-Zając Dominika, 2019. "On Permutation Location–Scale Tests," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 153-166, December.
    5. Alba Fernández, V. & Jiménez Gamero, M.D. & Muñoz Garcia, J., 2008. "A test for the two-sample problem based on empirical characteristic functions," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3730-3748, March.
    6. Rosa Arboretti & Elena Barzizza & Nicolò Biasetton & Riccardo Ceccato & Livio Corain & Luigi Salmaso, 2022. "A Multi-Aspect Permutation Test for Goodness-of-Fit Problems," Stats, MDPI, vol. 5(2), pages 1-11, June.
    7. Brombin, Chiara & Salmaso, Luigi, 2009. "Multi-aspect permutation tests in shape analysis with small sample size," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3921-3931, October.
    8. Pini, Alessia & Spreafico, Lorenzo & Vantini, Simone & Vietti, Alessandro, 2019. "Multi-aspect local inference for functional data: Analysis of ultrasound tongue profiles," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 162-185.

    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:taf:japsta:v:34:y:2007:i:2:p:203-216. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.