IDEAS home Printed from https://ideas.repec.org/a/spr/sistpr/v28y2025i1d10.1007_s11203-025-09325-w.html
   My bibliography  Save this article

Maximum spacing estimation for hidden Markov models

Author

Listed:
  • Kristi Kuljus

    (University of Tartu)

  • Bo Ranneby

    (Swedish University of Agricultural Sciences)

Abstract

This article generalizes the maximum spacing (MSP) method to dependent observations by considering hidden Markov models. The MSP method for estimating the model parameters is applied in two steps: at first the parameters of the marginal distribution of observations are estimated, in the second step the transition probabilities of the underlying Markov chain are estimated using the obtained marginal parameter estimates. We prove that the proposed MSP estimation procedure gives consistent estimators. The possibility of using the proposed estimation procedure in the context of model validation is investigated in simulation examples. It is demonstrated that when the observations are dependent, then taking into account the dependence structure by considering two-dimensional spacings provides additional information about a suitable number of mixture components in the model. The proposed estimation method is also applied in a real data example.

Suggested Citation

  • Kristi Kuljus & Bo Ranneby, 2025. "Maximum spacing estimation for hidden Markov models," Statistical Inference for Stochastic Processes, Springer, vol. 28(1), pages 1-31, April.
  • Handle: RePEc:spr:sistpr:v:28:y:2025:i:1:d:10.1007_s11203-025-09325-w
    DOI: 10.1007/s11203-025-09325-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11203-025-09325-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11203-025-09325-w?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:spr:sistpr:v:28:y:2025:i:1:d:10.1007_s11203-025-09325-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.