Relative weight analysis with residualization for detecting relevant non linear effects

Maikol Solís and Carlos Pasquier

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 23, 7553-7568

Abstract: Relative weight analysis is a classic tool for detecting whether one variable or interaction in a model is relevant. In this study, we focus on the construction of relative weights for non linear interactions using restricted cubic splines. Our aim is to provide an accessible method to analyze a multivariate model and identify one subset with the most representative set of variables. Furthermore, we developed a procedure for treating confirmed, exploratory, and interaction terms simultaneously in the residual weight analysis. The method residualizes the interactions properly against their main effects to maintain their true effects in the model. We tested this method using three simulated examples.

Date: 2025
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DOI: 10.1080/03610926.2025.2477824

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