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Solving problems in parameter redundancy using computer algebra

Author

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  • E. A. Catchpole
  • B. J. T. Morgan
  • A. Viallefont

Abstract

A model, involving a particular set of parameters, is said to be parameter redundant when the likelihood can be expressed in terms of a smaller set of parameters. In many important cases, the parameter redundancy of a model can be checked by evaluating the symbolic rank of a derivative matrix. We describe the main results, and show how to construct this matrix using the symbolic algebra package Maple. We apply the theory to examples from the mark-recapture field. General code is given which can be applied to other models.

Suggested Citation

  • E. A. Catchpole & B. J. T. Morgan & A. Viallefont, 2002. "Solving problems in parameter redundancy using computer algebra," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 625-636.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:625-636
    DOI: 10.1080/02664760120108601
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    References listed on IDEAS

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    1. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    2. E. A. Catchpole & P. M. Kgosi & B. J. T. Morgan, 2001. "On the Near-Singularity of Models for Animal Recovery Data," Biometrics, The International Biometric Society, vol. 57(3), pages 720-726, September.
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    Cited by:

    1. E. A. Catchpole & P. M. Kgosi & B. J. T. Morgan, 2001. "On the Near-Singularity of Models for Animal Recovery Data," Biometrics, The International Biometric Society, vol. 57(3), pages 720-726, September.
    2. Mark P Little & Wolfgang F Heidenreich & Guangquan Li, 2010. "Parameter Identifiability and Redundancy: Theoretical Considerations," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-6, January.
    3. Wen‐Han Hwang & Richard Huggins & Jakub Stoklosa, 2022. "A model for analyzing clustered occurrence data," Biometrics, The International Biometric Society, vol. 78(2), pages 598-611, June.

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