IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v41y2024i3d10.1007_s00357-023-09438-y.html
   My bibliography  Save this article

Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates

Author

Listed:
  • Salvatore D. Tomarchio

    (University of Catania)

  • Antonio Punzo

    (University of Catania)

  • Antonello Maruotti

    (LUMSA University)

Abstract

Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the covariates, which can be treated as fixed or random. Parsimony is achieved by using the eigen-decomposition of the components’ covariance matrices. This generates a different number of parsimonious models between the two families: 98 MV-HMRMs with fixed covariates and 9604 MV-HMRMs with random covariates. ECM algorithms are discussed for parameter estimation and, because of the high number of parsimonious models, convenient initialization, and fitting strategies are proposed. Our models are first applied to simulated data, and then to a four-way real dataset concerning the relationship between unemployment and labor force participation in the Italian provinces.

Suggested Citation

  • Salvatore D. Tomarchio & Antonio Punzo & Antonello Maruotti, 2024. "Matrix-Variate Hidden Markov Regression Models: Fixed and Random Covariates," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 429-454, November.
    • Handle: RePEc:spr:jclass:v:41:y:2024:i:3:d:10.1007_s00357-023-09438-y
    DOI: 10.1007/s00357-023-09438-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-023-09438-y
    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/s00357-023-09438-y?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:jclass:v:41:y:2024:i:3:d:10.1007_s00357-023-09438-y. 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.