IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v58y2004i2p220-233.html
   My bibliography  Save this article

An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models

Author

Listed:
  • Jeroen K. Vermunt

Abstract

It is shown how to implement an EM algorithm for maximum likelihood estimation of hierarchical nonlinear models for data sets consisting of more than two levels of nesting. This upward–downward algorithm makes use of the conditional independence assumptions implied by the hierarchical model. It cannot only be used for the estimation of models with a parametric specification of the random effects, but also to extend the two‐level nonparametric approach – sometimes referred to as latent class regression – to three or more levels. The proposed approach is illustrated with an empirical application.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:2:p:220-233
    DOI: 10.1046/j.0039-0402.2003.00257.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1046/j.0039-0402.2003.00257.x
    Download Restriction: no
    File URL: https://libkey.io/10.1046/j.0039-0402.2003.00257.x?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
    ---><---

    Citations

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


    Cited by:

    1. Hilger, James & Hanemann, Michael, 2006. "Heterogeneous Preferences for Water Quality: A Finite Mixture Model of Beach Recreation in Southern California," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt0565c0b2, Department of Agricultural & Resource Economics, UC Berkeley.
    2. Carlos Forero & Josué Almansa & Núria Adroher & Jeroen Vermunt & Gemma Vilagut & Ron Graaf & Josep-Maria Haro & Jordi Alonso Caballero, 2014. "Partial Likelihood Estimation of IRT Models with Censored Lifetime Data: An Application to Mental Disorders in the ESEMeD Surveys," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 470-488, July.
    3. Chan, Moon-tong & Yu, Dalei & Yau, Kelvin K.W., 2015. "Multilevel cumulative logistic regression model with random effects: Application to British social attitudes panel survey data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 173-186.
    4. 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.
    5. Dylan Molenaar & Conor Dolan & Paul Boeck, 2012. "The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions," Psychometrika, Springer;The Psychometric Society, vol. 77(3), pages 455-478, July.
    6. Begoña A. Farizo & John Joyce & Mario Soliño, 2014. "Dealing with Heterogeneous Preferences Using Multilevel Mixed Models," Land Economics, University of Wisconsin Press, vol. 90(1), pages 181-198.

    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:stanee:v:58:y:2004:i:2:p:220-233. 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.

    We have no bibliographic references for this item. You can help adding them by using 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=0039-0402 .

    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.