IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v31y2022i3d10.1007_s10260-021-00581-7.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-021-00581-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-021-00581-7?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. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
    Full references (including those not matched with items on IDEAS)

    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. Carolina Laureti & Ariane Szafarz, 2016. "The price of deposit liquidity: banks versus microfinance institutions," Applied Economics Letters, Taylor & Francis Journals, vol. 23(17), pages 1244-1249, November.
    2. Elias Christopher J., 2015. "Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison," Journal of Econometric Methods, De Gruyter, vol. 4(1), pages 153-161, January.
    3. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    4. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    5. Cyrus J. DiCiccio & Joseph P. Romano & Michael Wolf, 2016. "Improving weighted least squares inference," ECON - Working Papers 232, Department of Economics - University of Zurich, revised Nov 2017.
    6. Romano, Joseph P. & Wolf, Michael, 2017. "Resurrecting weighted least squares," Journal of Econometrics, Elsevier, vol. 197(1), pages 1-19.
    7. Fallesen, Peter & Geerdsen, Lars Pico & Imai, Susumu & Tranæs, Torben, 2018. "The effect of active labor market policies on crime: Incapacitation and program effects," Labour Economics, Elsevier, vol. 52(C), pages 263-286.
    8. Badi H. Baltagi & Chihwa Kao & Long Liu, 2013. "The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 241-270, September.
    9. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.
    10. Olivier Armantier, 2006. "Estimates of Own Lethal Risks and Anchoring Effects," Journal of Risk and Uncertainty, Springer, vol. 32(1), pages 37-56, January.
    11. Lee, Taewook, 2016. "Wild bootstrap Ljung–Box test for cross correlations of multivariate time series," Economics Letters, Elsevier, vol. 147(C), pages 59-62.
    12. Oc, Burak & Daniels, Michael A. & Diefendorff, James M. & Bashshur, Michael R. & Greguras, Gary J., 2020. "Humility breeds authenticity: How authentic leader humility shapes follower vulnerability and felt authenticity," Organizational Behavior and Human Decision Processes, Elsevier, vol. 158(C), pages 112-125.
    13. Yang He & Otávio Bartalotti, 2020. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," The Econometrics Journal, Royal Economic Society, vol. 23(2), pages 211-231.
    14. Nicolas DEBARSY & Cem ERTUR, 2016. "Interaction matrix selection in spatial econometrics with an application to growth theory," LEO Working Papers / DR LEO 2172, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    15. Manolopoulos Dimitris & Salavou Helen & Papadopoulos Andrew & Xenakis Michail, 2024. "Strategic Decision-Making and Performance in Social Enterprises: Process Dimensions and the Influence of Entrepreneurs’ Proactive Personality," Entrepreneurship Research Journal, De Gruyter, vol. 14(2), pages 631-675, April.
    16. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    17. Pavlidis Efthymios G & Paya Ivan & Peel David A, 2010. "Specifying Smooth Transition Regression Models in the Presence of Conditional Heteroskedasticity of Unknown Form," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(3), pages 1-40, May.
    18. Jianghao Chu & Tae-Hwy Lee & Aman Ullah & Haifeng Xu, 2020. "Exact Distribution of the F-statistic under Heteroskedasticity of Unknown Form for Improved Inference," Working Papers 202027, University of California at Riverside, Department of Economics.
    19. Eric Blankmeyer, 2018. "Measurement Errors as Bad Leverage Points," Papers 1807.02814, arXiv.org, revised Mar 2020.
    20. Rojas-Perilla, Natalia & Pannier, Sören & Schmid, Timo & Tzavidis, Nikos, 2017. "Data-driven transformations in small area estimation," Discussion Papers 2017/30, Free University Berlin, School of Business & Economics.

    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:stmapp:v:31:y:2022:i:3:d:10.1007_s10260-021-00581-7. 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.