IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v36y2025i7ne70045.html

A New Kernel Prediction Approach Under Kibria‐Lukman Methodology for Partially Linear Mixed Measurement Error Models

Author

Listed:
  • Özge Kuran
  • Seçil Yalaz

Abstract

In this study, we propose a new kernel prediction approach based on the Kibria‐Lukman methodology to reduce the effects of multicollinearity in partially linear mixed measurement error models. The performance of the proposed kernel Kibria‐Lukman estimator/predictor is evaluated and compared with several existing alternatives, including the kernel, kernel ridge, and kernel Liu estimators/predictors, using the matrix mean square error criterion. Furthermore, we examine the asymptotic normality of the proposed method and address the case of an unknown covariance matrix of measurement errors. To support the theoretical findings, both a real earthquake dataset and an extensive Monte Carlo simulation study are analyzed.

Suggested Citation

  • Özge Kuran & Seçil Yalaz, 2025. "A New Kernel Prediction Approach Under Kibria‐Lukman Methodology for Partially Linear Mixed Measurement Error Models," Environmetrics, John Wiley & Sons, Ltd., vol. 36(7), October.
    • Handle: RePEc:wly:envmet:v:36:y:2025:i:7:n:e70045
    DOI: 10.1002/env.70045
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.70045
    Download Restriction: no
    File URL: https://libkey.io/10.1002/env.70045?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
    ---><---

    References listed on IDEAS

    as
    1. M. Revan Özkale & Funda Can, 2017. "An evaluation of ridge estimator in linear mixed models: an example from kidney failure data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(12), pages 2251-2269, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.
    2. Özge Kuran & M. Revan Özkale, 2024. "The ridge prediction error sum of squares statistic in linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 87(7), pages 757-773, October.

    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:envmet:v:36:y:2025:i:7:n:e70045. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: http://www.interscience.wiley.com/jpages/1180-4009/ .

    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.