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G. Coenders and M. Saez, “Collinearity, Heteroscedasticity, and Outlier Diagnostics in Regression: Do They Always Offer What They Claim?” In: A. Ferligoj and A. Mrvar, Eds., New Approaches in Applied Statistics, Metodoloski Zvezki, Faculty of Social Sciences, Ljubljana, Slovenia, 2000, pp. 17-94.

has been cited by the following article:

• TITLE: Could Sequential Residual Centering Resolve Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom Clusters

  AUTHORS: Richard B. Francoeur

  KEYWORDS: Mean Centering; Multicollinearity; Moderated Regression; Statistical Interaction; Effect Modifier; Residual Centering; Symptom Cluster; Sickness Behavior; Malaise; Cancer

  JOURNAL NAME: Open Journal of Statistics, Vol.3 No.6A, December 27, 2013

  ABSTRACT: Multicollinearity constitutes shared variation among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of mean centering in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. Residual centering (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpretation of moderator effects. I propose and validate residual centering in sequential re-estimations of a moderated regression—sequential residual centering (SRC)—by revealing unbiased multicollinearity conditioning across the interaction and its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used to derive a lower standard error of its regression coefficient. A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.