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Reparameterization of nonlinear statistical models: a case study

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  • Gavin Ross
  • C. Sarada

Abstract

The importance of finding appropriate parameterizations for nonlinear statistical models is highlighted. The purpose of this paper is to explore the principles of reparameterization, using an example from real data. It is shown that stable parameterizations allow likelihood-based confidence intervals to be computed. Further, it is noted that the choice of error distribution may seriously affect the estimates and confidence intervals of quantities of interest. The influence of each observation on the estimation of each parameter is displayed for each error model. Multidimensional likelihood contours may be displayed pairwise using profile likelihood computations.

Suggested Citation

  • Gavin Ross & C. Sarada, 2010. "Reparameterization of nonlinear statistical models: a case study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2015-2026.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:12:p:2015-2026
    DOI: 10.1080/02664760903207332
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    References listed on IDEAS

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    1. Matis, James H. & Kiffe, Thomas R. & Matis, Timothy I. & Jackman, John A. & Singh, Harvir, 2007. "Population size models based on cumulative size, with application to aphids," Ecological Modelling, Elsevier, vol. 205(1), pages 81-92.
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    Cited by:

    1. Duarte, Belmiro P.M. & Atkinson, Anthony C. & Oliveira, Nuno M.C., 2023. "Optimum design for ill-conditioned models: K–optimality and stable parameterizations," LSE Research Online Documents on Economics 122986, London School of Economics and Political Science, LSE Library.

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