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Recycled two-stage estimation in nonlinear mixed effects regression models

Author

Listed:
  • Yue Zhang

    (Department of Mathematical Sciences, IUPUI)

  • Ben Boukai

    (Department of Mathematical Sciences, IUPUI)

Abstract

We consider a re-sampling scheme for estimation of the population parameters in the mixed-effects nonlinear regression models of the type used, for example, in clinical pharmacokinetics. We provide a two-stage estimation procedure which resamples (or recycles), via random weightings, the various parameter's estimates to construct consistent estimates of their respective sampling distributions. In particular, we establish under rather general distribution-free assumptions, the asymptotic normality and consistency of the standard two-stage estimates and of their resampled version and demonstrate the applicability of our proposed resampling methodology in a small simulation study. A detailed example based on real clinical pharmacokinetic data is also provided.

Suggested Citation

  • Yue Zhang & Ben Boukai, 2022. "Recycled two-stage estimation in nonlinear mixed effects regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 551-585, September.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:3:d:10.1007_s10260-021-00581-7
    DOI: 10.1007/s10260-021-00581-7
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    References listed on IDEAS

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    1. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
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