IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2007i9p4297-4307.html
   My bibliography  Save this article

Testing transformations for the linear mixed model

Author

Listed:
  • Gurka, Matthew J.
  • Edwards, Lloyd J.
  • Nylander-French, Leena

Abstract

No abstract is available for this item.

Suggested Citation

  • Gurka, Matthew J. & Edwards, Lloyd J. & Nylander-French, Leena, 2007. "Testing transformations for the linear mixed model," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4297-4307, May.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:9:p:4297-4307
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(06)00179-4
    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. Matthew J. Gurka & Lloyd J. Edwards & Keith E. Muller & Lawrence L. Kupper, 2006. "Extending the Box–Cox transformation to the linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(2), pages 273-288, March.
    2. S. R. Lipsitz & J. Ibrahim & G. Molenberghs, 2000. "Using a Box–Cox transformation in the analysis of longitudinal data with incomplete responses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(3), pages 287-296.
    3. Gurka, Matthew J., 2006. "Selecting the Best Linear Mixed Model Under REML," The American Statistician, American Statistical Association, vol. 60, pages 19-26, February.
    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. Jiang, Jiakun & Lin, Huazhen & Zhong, Qingzhi & Li, Yi, 2022. "Analysis of multivariate non-gaussian functional data: A semiparametric latent process approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. John A. D. Aston & Jeng‐Min Chiou & Jonathan P. Evans, 2010. "Linguistic pitch analysis using functional principal component mixed effect models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 297-317, March.
    3. Kazushi Maruo & Takaharu Yamabe & Yusuke Yamaguchi, 2017. "Statistical simulation based on right skewed distributions," Computational Statistics, Springer, vol. 32(3), pages 889-907, September.
    4. Patricia Dörr & Jan Pablo Burgard, 2019. "Data-driven transformations and survey-weighting for linear mixed models," Research Papers in Economics 2019-16, University of Trier, Department of Economics.
    5. Huapeng Li & Yukun Liu & Riquan Zhang, 2019. "Small area estimation under transformed nested-error regression models," Statistical Papers, Springer, vol. 60(4), pages 1397-1418, August.
    6. Nora Würz & Timo Schmid & Nikos Tzavidis, 2022. "Estimating regional income indicators under transformations and access to limited population auxiliary information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1679-1706, October.
    7. Tang, Niansheng & Wu, Ying & Chen, Dan, 2018. "Semiparametric Bayesian analysis of transformation linear mixed models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 225-240.
    8. Tzavidis, Nikos & Zhang, Li-Chun & Luna Hernandez, Angela & Schmid, Timo & Rojas-Perilla, Natalia, 2016. "From start to finish: A framework for the production of small area official statistics," Discussion Papers 2016/13, Free University Berlin, School of Business & Economics.
    9. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    10. Shonosuke Sugasawa & Tatsuya Kubokawa, 2015. "Box-Cox Transformed Linear Mixed Models for Positive-Valued and Clustered Data," CIRJE F-Series CIRJE-F-957, CIRJE, Faculty of Economics, University of Tokyo.
    11. Joseph G. Ibrahim & Hongtu Zhu & Ramon I. Garcia & Ruixin Guo, 2011. "Fixed and Random Effects Selection in Mixed Effects Models," Biometrics, The International Biometric Society, vol. 67(2), pages 495-503, June.
    12. Haitao Zheng & Jie Hu & Rong Guan & Shanshan Wang, 2016. "Examining Determinants of CO 2 Emissions in 73 Cities in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    13. Natalia Rojas‐Perilla & Sören Pannier & Timo Schmid & Nikos Tzavidis, 2020. "Data‐driven transformations in small area estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 121-148, January.
    14. Paul Walter & Marcus Groß & Timo Schmid & Nikos Tzavidis, 2021. "Domain prediction with grouped income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1501-1523, October.
    15. Dimova, Rositsa B. & Markatou, Marianthi & Talal, Andrew H., 2011. "Information methods for model selection in linear mixed effects models with application to HCV data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2677-2697, September.
    16. Hiroshi Ishijima & Akira Maeda, 2015. "Real Estate Pricing Models: Theory, Evidence, and Implementation," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 22(4), pages 369-396, November.
    17. Tan, W. D. & Gan, F. F. & Chang, T. C., 2004. "Using normal quantile plot to select an appropriate transformation to achieve normality," Computational Statistics & Data Analysis, Elsevier, vol. 45(3), pages 609-619, April.
    18. Giulia Fuochi & Alberto Voci, 2021. "Dealing with the Ups and Downs of Life: Positive Dispositions in Coping with Negative and Positive Events and Their Relationships with Well-Being Indicators," Journal of Happiness Studies, Springer, vol. 22(6), pages 2435-2456, August.
    19. Villandré Luc & Hutcheon Jennifer A & Perez Trejo Maria Esther & Abenhaim Haim & Jacobsen Geir & Platt Robert W, 2011. "Modeling Fetal Weight for Gestational Age: A Comparison of a Flexible Multi-level Spline-based Model with Other Approaches," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-26, August.
    20. Ileana Baldi & Eva Pagano & Paola Berchialla & Alessandro Desideri & Alberto Ferrando & Franco Merletti & Dario Gregori, 2013. "Modeling healthcare costs in simultaneous presence of asymmetry, heteroscedasticity and correlation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 298-310, February.

    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:51:y:2007:i:9:p:4297-4307. 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.