IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v44y2017i12p2251-2269.html
   My bibliography  Save this article

An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

Author

Listed:
  • M. Revan Özkale
  • Funda Can

Abstract

This paper is concerned with the ridge estimation of fixed and random effects in the context of Henderson's mixed model equations in the linear mixed model. For this purpose, a penalized likelihood method is proposed. A linear combination of ridge estimator for fixed and random effects is compared to a linear combination of best linear unbiased estimator for fixed and random effects under the mean-square error (MSE) matrix criterion. Additionally, for choosing the biasing parameter, a method of MSE under the ridge estimator is given. A real data analysis is provided to illustrate the theoretical results and a simulation study is conducted to characterize the performance of ridge and best linear unbiased estimators approach in the linear mixed model.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:12:p:2251-2269
    DOI: 10.1080/02664763.2016.1252732
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1252732
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1252732?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.

    References listed on IDEAS

    as
    1. Eliot Melissa & Ferguson Jane & Reilly Muredach P. & Foulkes Andrea S., 2011. "Ridge Regression for Longitudinal Biomarker Data," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-11, September.
    2. Hu Yang & Huiliang Ye & Kai Xue, 2014. "A Further Study of Predictions in Linear Mixed Models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(20), pages 4241-4252, October.
    3. Liu, Xu-Qing & Rong, Jian-Ying & Liu, Xiu-Ying, 2008. "Best linear unbiased prediction for linear combinations in general mixed linear models," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1503-1517, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    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. Ramalingam Shanmugam & Arashi M & Salarzadeh Jenatabadi H, 2018. "Longitudinal Data Analysis Using Liu Regression," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(5), pages 102-105, July.
    2. Shu Ming YANG & Su Hua ZHANG & Tao YANG & Li WANG, 2018. "Detection of QTLs for cold tolerance at the booting stage in near-isogenic lines derived from rice landrace Lijiangxintuanheigu," Czech Journal of Genetics and Plant Breeding, Czech Academy of Agricultural Sciences, vol. 54(3), pages 93-100.
    3. Liu, Xu-Qing & Wang, Dong-Dong & Rong, Jian-Ying, 2009. "Quadratic prediction and quadratic sufficiency in finite populations," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1979-1988, October.
    4. Dermoune, Azzouz & Rahmania, Nadji & Wei, Tianwen, 2012. "General linear mixed model and signal extraction problem with constraint," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 311-321.
    5. Caroline Bazzoli & Sophie Lambert-Lacroix & Marie-José Martinez, 2023. "Partial least square based approaches for high-dimensional linear mixed models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 769-786, September.
    6. Nesrin Güler & Melek Eriş Büyükkaya & Melike Yiğit, 2022. "Comparison of Covariance Matrices of Predictors in Seemingly Unrelated Regression Models," Indian Journal of Pure and Applied Mathematics, Springer, vol. 53(3), pages 801-809, September.
    7. S. J. Haslett & X. Q. Liu & A. Markiewicz & S. Puntanen, 2020. "Some properties of linear sufficiency and the BLUPs in the linear mixed model," Statistical Papers, Springer, vol. 61(1), pages 385-401, February.
    8. Yongge Tian, 2015. "A new derivation of BLUPs under random-effects model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 905-918, November.
    9. Laura M. Grajeda & Lisa M. Thompson & William Arriaga & Eduardo Canuz & Saad B. Omer & Michael Sage & Eduardo Azziz-Baumgartner & Joe P. Bryan & John P. McCracken, 2020. "Effectiveness of Gas and Chimney Biomass Stoves for Reducing Household Air Pollution Pregnancy Exposure in Guatemala: Sociodemographic Effect Modifiers," IJERPH, MDPI, vol. 17(21), pages 1-14, October.
    10. Mozhgan Taavoni & Mohammad Arashi & Samuel Manda, 2023. "Multicollinearity and Linear Predictor Link Function Problems in Regression Modelling of Longitudinal Data," Mathematics, MDPI, vol. 11(3), pages 1-9, January.
    11. Jan Pablo Burgard & Joscha Krause & Ralf Münnich, 2019. "Penalized Small Area Models for the Combination of Unit- and Area-level Data," Research Papers in Economics 2019-05, University of Trier, Department of Economics.
    12. Xiaojun Shi & Hiroshi Tsuji & Shunming Zhang, 2012. "Introducing Heterogeneity of Managers' Attitude into Behavioral Risk Scoring for Software Offshoring," Systems Research and Behavioral Science, Wiley Blackwell, vol. 29(3), pages 299-316, May.
    13. Liu, Xin & Wang, Qing-Wen, 2013. "Equality of the BLUPs under the mixed linear model when random components and errors are correlated," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 297-309.

    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:japsta:v:44:y:2017:i:12:p:2251-2269. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.