IDEAS home Printed from https://ideas.repec.org/a/bpj/ecqcon/v37y2022i1p21-43n1.html
   My bibliography  Save this article

Estimations of Means and Variances in a Markov Linear Model

Author

Listed:
  • Gutierrez Abraham

    (Institute of Discrete Mathematics, Graz University of Technology, Graz, Austria)

  • Müller Sebastian

    (I2M – UMR 7373, Aix-Marseille Université, CNRS, Centrale Marseille, Marseille, France)

Abstract

Multivariate regression models and ANOVA are probably the most frequently applied methods of all statistical analyses. We study the case where the predictors are qualitative variables and the response variable is quantitative. In this case, we propose an alternative to the classic approaches that does not assume homoscedasticity but assumes that a Markov chain can describe the covariates’ correlations. This approach transforms the dependent covariates using a change of measure to independent covariates. The transformed estimates allow a pairwise comparison of the mean and variance of the contribution of different values of the covariates. We show that, under standard moment conditions, the estimators are asymptotically normally distributed. We test our method with data from simulations and apply it to several classic data sets.

Suggested Citation

  • Gutierrez Abraham & Müller Sebastian, 2022. "Estimations of Means and Variances in a Markov Linear Model," Stochastics and Quality Control, De Gruyter, vol. 37(1), pages 21-43, June.
    • Handle: RePEc:bpj:ecqcon:v:37:y:2022:i:1:p:21-43:n:1
    DOI: 10.1515/eqc-2022-0004
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/eqc-2022-0004
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/eqc-2022-0004?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:bpj:ecqcon:v:37:y:2022:i:1:p:21-43:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.