IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v28y2013i4p1549-1570.html
   My bibliography  Save this article

Nonlinear nonparametric mixed-effects models for unsupervised classification

Author

Listed:
  • Laura Azzimonti
  • Francesca Ieva
  • Anna Maria Paganoni

Abstract

In this work we propose a novel EM method for the estimation of nonlinear nonparametric mixed-effects models, aimed at unsupervised classification. We perform simulation studies in order to evaluate the algorithm performance and we apply this new procedure to a real dataset. Copyright Springer-Verlag 2013

Suggested Citation

  • Laura Azzimonti & Francesca Ieva & Anna Maria Paganoni, 2013. "Nonlinear nonparametric mixed-effects models for unsupervised classification," Computational Statistics, Springer, vol. 28(4), pages 1549-1570, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1549-1570
    DOI: 10.1007/s00180-012-0366-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-012-0366-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-012-0366-5?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. Tze Leung Lai, 2003. "Nonparametric estimation in nonlinear mixed effects models," Biometrika, Biometrika Trust, vol. 90(1), pages 1-13, March.
    2. Jeroen K. Vermunt, 2004. "An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(2), pages 220-233, May.
    3. Kuhn, E. & Lavielle, M., 2005. "Maximum likelihood estimation in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1020-1038, June.
    4. Alison L. Gibbs & Francis Edward Su, 2002. "On Choosing and Bounding Probability Metrics," International Statistical Review, International Statistical Institute, vol. 70(3), pages 419-435, December.
    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. Lu Tang & Ling Zhou & Peter X. K. Song, 2019. "Fusion learning algorithm to combine partially heterogeneous Cox models," Computational Statistics, Springer, vol. 34(1), pages 395-414, March.
    2. Elson Tomás & Susana Vinga & Alexandra M. Carvalho, 2017. "Unsupervised learning of pharmacokinetic responses," Computational Statistics, Springer, vol. 32(2), pages 409-428, June.
    3. Leonardo Grilli & Carla Rampichini, 2015. "Specification of random effects in multilevel models: a review," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 967-976, May.
    4. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

    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. Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
    2. Oyama, Daisuke & Tercieux, Olivier, 2012. "On the strategic impact of an event under non-common priors," Games and Economic Behavior, Elsevier, vol. 74(1), pages 321-331.
    3. Crimaldi, Irene & Dai Pra, Paolo & Minelli, Ida Germana, 2016. "Fluctuation theorems for synchronization of interacting Pólya’s urns," Stochastic Processes and their Applications, Elsevier, vol. 126(3), pages 930-947.
    4. Munch, Jakob R. & Nguyen, Daniel X., 2014. "Decomposing firm-level sales variation," Journal of Economic Behavior & Organization, Elsevier, vol. 106(C), pages 317-334.
    5. Fabian R. Pieroth & Tuomas Sandholm, 2024. "Verifying Approximate Equilibrium in Auctions," Papers 2408.11445, arXiv.org.
    6. Fenner, Trevor & Levene, Mark & Loizou, George, 2010. "Predicting the long tail of book sales: Unearthing the power-law exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2416-2421.
    7. Baey, Charlotte & Cournède, Paul-Henry & Kuhn, Estelle, 2019. "Asymptotic distribution of likelihood ratio test statistics for variance components in nonlinear mixed effects models," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 107-122.
    8. Takashi Kamihigashi & John Stachurski, 2017. "Some Unified Results for Classical and Monotone Markov Chain Theory," Discussion Paper Series DP2017-02, Research Institute for Economics & Business Administration, Kobe University.
    9. Bertanha, Marinho & Moreira, Marcelo J., 2020. "Impossible inference in econometrics: Theory and applications," Journal of Econometrics, Elsevier, vol. 218(2), pages 247-270.
    10. HAEDO, Christian & MOUCHART, Michel, 2012. "A stochastic independence approach for different measures of concentration and specialization," LIDAM Discussion Papers CORE 2012025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. Schneider, Judith C. & Schweizer, Nikolaus, 2015. "Robust measurement of (heavy-tailed) risks: Theory and implementation," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 183-203.
    12. Daniel B. Reeves & Christian Gaebler & Thiago Y. Oliveira & Michael J. Peluso & Joshua T. Schiffer & Lillian B. Cohn & Steven G. Deeks & Michel C. Nussenzweig, 2023. "Impact of misclassified defective proviruses on HIV reservoir measurements," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    13. Gerhold, Stefan & Gülüm, I. Cetin, 2019. "Peacocks nearby: Approximating sequences of measures," Stochastic Processes and their Applications, Elsevier, vol. 129(7), pages 2406-2436.
    14. Boubacar Mainassara, Y. & Carbon, M. & Francq, C., 2012. "Computing and estimating information matrices of weak ARMA models," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 345-361.
    15. Xuejun Zhao & Ruihao Zhu & William B. Haskell, 2022. "Learning to Price Supply Chain Contracts against a Learning Retailer," Papers 2211.04586, arXiv.org.
    16. Ana C. Gómez Ugarte Valerio & Ugofilippo Basellini & Carlo G. Camarda & Fanny Janssen & Emilio Zagheni, 2024. "Reassessing socioeconomic inequalities in mortality via distributional similarities," MPIDR Working Papers WP-2024-007, Max Planck Institute for Demographic Research, Rostock, Germany.
    17. Borgonovo, E. & Zentner, I. & Pellegri, A. & Tarantola, S. & de Rocquigny, E., 2013. "On the importance of uncertain factors in seismic fragility assessment," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 66-76.
    18. Gurdip Bakshi & Xiaohui Gao & George Panayotov, 2021. "A Theory of Dissimilarity Between Stochastic Discount Factors," Management Science, INFORMS, vol. 67(7), pages 4602-4622, July.
    19. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    20. Huang, Xianzheng, 2011. "Detecting random-effects model misspecification via coarsened data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 703-714, January.

    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:spr:compst:v:28:y:2013:i:4:p:1549-1570. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.