IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v24y1995i1p49-56.html
   My bibliography  Save this article

Estimation of nonlinear random coefficient models

Author

Listed:
  • Ramos, Rogelio Q.
  • Pantula, Sastry G.

Abstract

Nonlinear random coefficient models are found to be useful in growth studies and pharmacokinetic experiments. Several methods exist in the literature for estimating the parameters of such models. The properties of the estimators are not well studied. In this paper we summarize different estimation methods and examine some of their properties. We give an example that shows that most of the commonly used estimators are inconsistent.

Suggested Citation

  • Ramos, Rogelio Q. & Pantula, Sastry G., 1995. "Estimation of nonlinear random coefficient models," Statistics & Probability Letters, Elsevier, vol. 24(1), pages 49-56, July.
  • Handle: RePEc:eee:stapro:v:24:y:1995:i:1:p:49-56
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(94)00147-Z
    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.

    Citations

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


    Cited by:

    1. Núñez, Olivier, 1998. "Asymptotic properties for a simulated pseudo maximum likelihood estimator," DES - Working Papers. Statistics and Econometrics. WS 6266, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Concordet, Didier & Nunez, Olivier G., 2002. "A simulated pseudo-maximum likelihood estimator for nonlinear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 39(2), pages 187-201, April.
    3. Didier Concordet & Olivier G. Nunez, 2000. "Calibration for Nonlinear Mixed Effects Models: An Application to the Withdrawal Time Prediction," Biometrics, The International Biometric Society, vol. 56(4), pages 1040-1046, December.
    4. Ge, Zhiyu & J. Bickel, Peter & A. Rice, John, 2004. "An approximate likelihood approach to nonlinear mixed effects models via spline approximation," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 747-776, July.

    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:stapro:v:24:y:1995:i:1:p:49-56. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.