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Estimation of nonlinear random coefficient models

Author

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  • 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
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    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.

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