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Model Interpretation from the Additive Elements of the Likelihood Function

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  • Joe Whittaker

Abstract

The interpretation of a fitted statistical model such as the classical linear or the generalized linear model Is substantially clarified by a full partitioning of the maximized log‐likelihood ratio test statistic Into additive elements. This method generalizes the regression elements of Newton and Spurrell (1967) used to aid interpretation of regression equations. The primary elements measure the unique contribution of each explanatory variable whereas the secondary and higher order elements measure the effective balance In the observed design. It Is remarked herein that the elements correspond to the parameters of a saturated factorial model fitted to the likelihood function. This permits a coherent computational procedure. Examples are taken from some well‐analysed data sets Illustrating the interpretation of regression and log‐linear models.

Suggested Citation

  • Joe Whittaker, 1984. "Model Interpretation from the Additive Elements of the Likelihood Function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 33(1), pages 52-64, March.
    • Handle: RePEc:bla:jorssc:v:33:y:1984:i:1:p:52-64
    DOI: 10.2307/2347663
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    1. Alexander C Keyel & Oliver Elison Timm & P Bryon Backenson & Catharine Prussing & Sarah Quinones & Kathleen A McDonough & Mathias Vuille & Jan E Conn & Philip M Armstrong & Theodore G Andreadis & Laur, 2019. "Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-32, June.

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