IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i7p2573-2582.html
   My bibliography  Save this article

Improved testing inference in mixed linear models

Author

Listed:
  • Melo, Tatiane F.N.
  • Ferrari, Silvia L.P.
  • Cribari-Neto, Francisco

Abstract

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62, 827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed.

Suggested Citation

  • Melo, Tatiane F.N. & Ferrari, Silvia L.P. & Cribari-Neto, Francisco, 2009. "Improved testing inference in mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2573-2582, May.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:7:p:2573-2582
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00575-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Francisco Cribari-Neto & Spyros Zarkos, 2003. "Econometric and Statistical Computing Using Ox," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 277-295, June.
    2. David M. Zucker & Offer Lieberman & Orly Manor, 2000. "Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 827-838.
    3. Cysneiros, Audrey H.M.A. & Ferrari, Silvia L.P., 2006. "An improved likelihood ratio test for varying dispersion in exponential family nonlinear models," Statistics & Probability Letters, Elsevier, vol. 76(3), pages 255-265, February.
    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. Stein, Markus Chagas & da Silva, Michel Ferreira & Duczmal, Luiz Henrique, 2014. "Alternatives to the usual likelihood ratio test in mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 184-197.
    2. Mariana C. Araújo & Audrey H. M. A. Cysneiros & Lourdes C. Montenegro, 2020. "Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models," Statistical Papers, Springer, vol. 61(1), pages 167-188, February.
    3. Yoshifumi Ukyo & Hisashi Noma & Kazushi Maruo & Masahiko Gosho, 2019. "Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis," Stats, MDPI, vol. 2(2), pages 1-15, March.

    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. Ito, Tsubasa & Sugasawa, Shonosuke, 2021. "Improved confidence regions in meta-analysis of diagnostic test accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 153(C).
    2. Ospina, Raydonal & Cribari-Neto, Francisco & Vasconcellos, Klaus L.P., 2006. "Improved point and interval estimation for a beta regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 960-981, November.
    3. Zeileis, Achim, 2004. "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i10).
    4. Lemonte, Artur J. & Cribari-Neto, Francisco & Vasconcellos, Klaus L.P., 2007. "Improved statistical inference for the two-parameter Birnbaum-Saunders distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4656-4681, May.
    5. Kulaksizoglu, Tamer, 2015. "Object-Oriented Econometrics with Ox," MPRA Paper 62545, University Library of Munich, Germany.
    6. Sébastien Laurent & Jean‐Pierre Urbain, 2005. "Bridging the gap between Ox and Gauss using OxGauss," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 131-139, January.
    7. Yoshifumi Ukyo & Hisashi Noma & Kazushi Maruo & Masahiko Gosho, 2019. "Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis," Stats, MDPI, vol. 2(2), pages 1-15, March.
    8. Mariana C. Araújo & Audrey H. M. A. Cysneiros & Lourdes C. Montenegro, 2020. "Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models," Statistical Papers, Springer, vol. 61(1), pages 167-188, February.
    9. David M. Zucker & Jonathan Denne, 2002. "Sample–Size Redetermination for Repeated Measures Studies," Biometrics, The International Biometric Society, vol. 58(3), pages 548-559, September.
    10. Ferrari, Silvia L.P. & Cysneiros, Audrey H.M.A., 2008. "Skovgaard's adjustment to likelihood ratio tests in exponential family nonlinear models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3047-3055, December.
    11. Lemonte, Artur J. & Ferrari, Silvia L.P. & Cribari-Neto, Francisco, 2010. "Improved likelihood inference in Birnbaum-Saunders regressions," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1307-1316, May.
    12. Vargas, Tiago M. & Ferrari, Silvia L.P. & Lemonte, Artur J., 2014. "Improved likelihood inference in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 110-124.
    13. Barreto-Souza, Wagner & Cribari-Neto, Francisco, 2009. "A generalization of the exponential-Poisson distribution," Statistics & Probability Letters, Elsevier, vol. 79(24), pages 2493-2500, December.
    14. Stein, Markus Chagas & da Silva, Michel Ferreira & Duczmal, Luiz Henrique, 2014. "Alternatives to the usual likelihood ratio test in mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 184-197.
    15. Maus, S. & Peters, H.J.M. & Storcken, A.J.A., 2004. "Minimal manipulability: anonymity and surjectivity," Research Memorandum 007, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    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:eee:csdana:v:53:y:2009:i:7:p:2573-2582. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.