Reducing multi-collinearity in GLMS with categorical covariates

Defen Peng () and Gilbert MacKenzie ()
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Defen Peng: University of British Columbia
Gilbert MacKenzie: University of Limerick

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 6, No 9, 983-1003

Abstract: Abstract When dealing with GLMs with categorical covariates we show that varying the reference subclasses leads to different variance–covariance matrices and develop a relation between a measure of precision and a measure of multi-collinearity, by analysis and simulation. The net result is that we are able to demonstrate, inter alia, how to reduce multi-collinearity by a judicious choice of reference subclasses in GLMs with categorical covariates. We develop estimators for the discrete choice minima of our measures and evaluate their performance in a wide class of GLM structures likely to arise in practice.

Keywords: Categorical variables; Discrete choice minimum; GLMs; Global minimum; Multi-collinearity; Precision (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00184-024-00980-2

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