IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v71y2015i2p333-343.html
   My bibliography  Save this article

Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators

Author

Listed:
  • Ana Arribas-Gil
  • Rolando De la Cruz
  • Emilie Lebarbier
  • Cristian Meza

Abstract

No abstract is available for this item.

Suggested Citation

  • Ana Arribas-Gil & Rolando De la Cruz & Emilie Lebarbier & Cristian Meza, 2015. "Classification of longitudinal data through a semiparametric mixed-effects model based on lasso-type estimators," Biometrics, The International Biometric Society, vol. 71(2), pages 333-343, June.
  • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:333-343
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12280
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Ma, Ping & Zhong, Wenxuan, 2008. "Penalized Clustering of Large-Scale Functional Data With Multiple Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 625-636, June.
    2. Liu, Wei & Wu, Lang, 2008. "A semiparametric nonlinear mixed-effects model with non-ignorable missing data and measurement errors for HIV viral data," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 112-122, September.
    3. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
    4. Kristien Wouters & Abdellah Ahnaou & Jose Cortinas Abrahantes & Geert Molenberghs & Helena Geys & Luc Bijnens & Wilhelmus H. I. M. Drinkenburg, 2007. "Psychotropic drug classification based on sleep–wake behaviour of rats," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 223-234, March.
    5. Wei Liu & Lang Wu, 2007. "Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses," Biometrics, The International Biometric Society, vol. 63(2), pages 342-350, June.
    6. Gareth M. James & Trevor J. Hastie, 2001. "Functional linear discriminant analysis for irregularly sampled curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 533-550.
    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. Sami Leon & Jingxuan Ren & Regine Choe & Tong Tong Wu, 2022. "Semiparametric mixed-effects model for analysis of non-invasive longitudinal hemodynamic responses during bone graft healing," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-13, April.

    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. Zhang, Xin & Jeske, Daniel R. & Li, Jun & Wong, Vance, 2016. "A sequential logistic regression classifier based on mixed effects with applications to longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 238-249.
    2. Luts, Jan & Molenberghs, Geert & Verbeke, Geert & Van Huffel, Sabine & Suykens, Johan A.K., 2012. "A mixed effects least squares support vector machine model for classification of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 611-628.
    3. Hongxiao Zhu & Philip J. Brown & Jeffrey S. Morris, 2012. "Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models," Biometrics, The International Biometric Society, vol. 68(4), pages 1260-1268, December.
    4. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    5. repec:cte:wsrepe:24606 is not listed on IDEAS
    6. Li, Pai-Ling & Chiou, Jeng-Min & Shyr, Yu, 2017. "Functional data classification using covariate-adjusted subspace projection," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 21-34.
    7. repec:cte:wsrepe:ws120906 is not listed on IDEAS
    8. Carlo Sguera & Pedro Galeano & Rosa Lillo, 2014. "Spatial depth-based classification for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 725-750, December.
    9. Huang Yangxin & Chen Ren & Dagne Getachew, 2011. "Simultaneous Bayesian Inference for Linear, Nonlinear and Semiparametric Mixed-Effects Models with Skew-Normality and Measurement Errors in Covariates," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-28, January.
    10. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    11. Llop, P. & Forzani, L. & Fraiman, R., 2011. "On local times, density estimation and supervised classification from functional data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 73-86, January.
    12. Weishampel, Anthony & Staicu, Ana-Maria & Rand, William, 2023. "Classification of social media users with generalized functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    13. Lu, Xiaosun & Huang, Yangxin & Zhu, Yiliang, 2016. "Finite mixture of nonlinear mixed-effects joint models in the presence of missing and mismeasured covariate, with application to AIDS studies," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 119-130.
    14. Shaikh Mateen & McNicholas Paul D & Desmond Anthony F, 2010. "A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-17, March.
    15. Chen, Xue-Dong & Tang, Nian-Sheng, 2010. "Bayesian analysis of semiparametric reproductive dispersion mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2145-2158, September.
    16. Luts, Jan & Ormerod, John T., 2014. "Mean field variational Bayesian inference for support vector machine classification," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 163-176.
    17. Hidetoshi Matsui & Takamitsu Araki & Sadanori Konishi, 2011. "Multiclass Functional Discriminant Analysis and Its Application to Gesture Recognition," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 227-243, July.
    18. S. Rao Jammalamadaka & Md. Aleemuddin Siddiqi & Kaushik Ghosh, 2009. "Analysis of microtubule dynamics using growth curve models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(6), pages 621-631.
    19. Cai, Wei & Guan, Guoyu & Pan, Rui & Zhu, Xuening & Wang, Hansheng, 2018. "Network linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 32-44.
    20. Antonio Elías & Raúl Jiménez & Han Lin Shang, 2023. "Depth-based reconstruction method for incomplete functional data," Computational Statistics, Springer, vol. 38(3), pages 1507-1535, September.
    21. Andrés Alonso & David Casado & Sara López-Pintado & Juan Romo, 2014. "Robust Functional Supervised Classification for Time Series," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 325-350, October.
    22. Park, Yeonjoo & Simpson, Douglas G., 2019. "Robust probabilistic classification applicable to irregularly sampled functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 37-49.

    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:bla:biomet:v:71:y:2015:i:2:p:333-343. 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.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.