IDEAS home Printed from https://ideas.repec.org/a/taf/lstaxx/v51y2021i22p7801-7818.html
   My bibliography  Save this article

Maximum likelihood estimation of the change point in stationary state of auto regressive moving average (ARMA) models, using SVD-based smoothing

Author

Listed:
  • Raza Sheikhrabori
  • Majid Aminnayeri

Abstract

The change point estimation concept is usually useful in time series models. This concept helps to decrease the decision making or production costs by monitoring the stock market and production lines. It is also applied in several fields such as Financing and Quality Control. In this paper, it is assumed that the ARMA (1) model exists between the sample statistics of x¯ control chart. A maximum likelihood technique is developed to estimate the change point at which the stationary ARMA (1) model changes into a non-stationary process. For the estimation of unknown parameters after the change point, the Smoothing of Dynamic Linear Model has been used based on singular value decomposition. It is assumed that there exists a correlation between sample statistics. Simulation results show the effectiveness of the estimators proposed in this paper to estimate the change point of the stationary state in ARMA (1) model. An example is provided to illustrate the application.

Suggested Citation

  • Raza Sheikhrabori & Majid Aminnayeri, 2021. "Maximum likelihood estimation of the change point in stationary state of auto regressive moving average (ARMA) models, using SVD-based smoothing," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(22), pages 7801-7818, September.
    • Handle: RePEc:taf:lstaxx:v:51:y:2021:i:22:p:7801-7818
    DOI: 10.1080/03610926.2021.1881120
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03610926.2021.1881120
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03610926.2021.1881120?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.

    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:taf:lstaxx:v:51:y:2021:i:22:p:7801-7818. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/lsta .

    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.