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False parsimony and its detection with GLMs

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  • Youngjo Lee
  • John Nelder

Abstract

A search for a good parsimonious model is often required in data analysis. However, unfortunately we may end up with a falsely parsimonious model. Misspecification of the variance structure causes a loss of efficiency in regression estimation and this can lead to large standard-error estimates, producing possibly false parsimony. With generalized linear models (GLMs) we can keep the link function fixed while changing the variance function, thus allowing us to recognize false parsimony caused by such increased standard errors. With data transformation, any change of transformation automatically changes the scale for additivity, making false parsimony hard to recognize.

Suggested Citation

  • Youngjo Lee & John Nelder, 2003. "False parsimony and its detection with GLMs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 477-483.
    • Handle: RePEc:taf:japsta:v:30:y:2003:i:5:p:477-483
    DOI: 10.1080/0266476032000053637
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