IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v31y2015i2p160-177.html
   My bibliography  Save this article

Using statistical shape theory for the monitoring of nonlinear profiles

Author

Listed:
  • Javier Cano
  • Javier M. Moguerza
  • Stelios Psarakis
  • Athanasios N. Yannacopoulos

Abstract

The quality of a process or product can be characterized by a functional relationship between a response variable and one or more explanatory variables. In this work, we develop a novel hybrid nonparametric–parametric procedure for the monitoring of nonlinear profiles, that is, realizations of a noisy nonlinear functional relationship between variables. In particular, we focus on the ‘shape’ property of profiles as a way of measuring their quality. Starting from a nonparametric reference curve, we select our model from a universe of parametric deformations of such a curve with the property of preserving certain important shape characteristics. To this aim, we design a metric based on the solution of a related optimization problem. In addition, we show that the problem is well posed from a theoretical point of view. Finally, we illustrate the performance of the proposal with numerical examples from simulated and real environments. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Javier Cano & Javier M. Moguerza & Stelios Psarakis & Athanasios N. Yannacopoulos, 2015. "Using statistical shape theory for the monitoring of nonlinear profiles," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(2), pages 160-177, March.
    • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:2:p:160-177
    DOI: 10.1002/asmb.2059
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2059
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2059?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
    ---><---

    Citations

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


    Cited by:

    1. Georgios I. Papayiannis & Stelios Psarakis & Athanasios N. Yannacopoulos, 2023. "Modelling of Functional Profiles and Explainable Shape Shifts Detection: An Approach Combining the Notion of the Fréchet Mean with the Shape-Invariant Model," Mathematics, MDPI, vol. 11(21), pages 1-24, October.
    2. Moura Neto, F. & Souza, P. & de Magalhães, M.S., 2019. "Determining baseline profile by diffusion maps," European Journal of Operational Research, Elsevier, vol. 279(1), pages 107-123.

    More about this item

    Statistics

    Access and download statistics

    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:wly:apsmbi:v:31:y:2015:i:2:p:160-177. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.