The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations

By Carl F. Mela, Praveen K. Kopalle. 2002

303 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a97b257943cf1b3eb2238aaaba12d85d2c830e4b

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Citations in the corpus

Cutting through Content Clutter: How Speech and Image Acts Drive Consumer Sharing of Social Media Brand Messages (2019, background): “Table 3 provides the variable means, standard deviations, and correlations; we find no collinearity issues (Mela and Kopalle 2002).”
Testing the ability of species distribution models to infer variable importance (2019, background): “There was no strong trend in differences between positive and negative correlation (cf. Mela & Kapolle 2002), although responses were somewhat asymmetric for each model.”
Impact of multicollinearity on small sample hydrologic regression models (2013, result): “…consistent with reported results that when multicollinearity is present in a model, OLS produces parameter estimators with inflated variances, and that the use of PCR or PLS can reduce these parameter estimator variances [Kiers and Smilde, 2007; Mela and Kopalle, 2002; Frank and Friedman, 1993].”
Extracting the Variance Inflation Factor and Other Multicollinearity Diagnostics from Typical Regression Results (2017, background): “Much has been written about the impact of multicollinearity on regression analyses elsewhere (e.g., Graham, 2003; Mela & Kopalle, 2002).”
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013, background): “Guidance on the relevance of asymmetric effects of positive and negative correlations: Mela & Kopalle (2002) report that different diagnostic tests for collinearity may yield 840 different results.”