Which of the following actions does not help address multicollinearity?

The action that does not help address multicollinearity is transforming the response variable with a concave function. Multicollinearity occurs when two or more predictor variables in a statistical model are highly correlated, which can lead to unreliable coefficient estimates and difficulties in understanding the individual contributions of the predictors.

Transforming the response variable, whether with a concave function or other types of transformations, does not directly address multicollinearity among the predictors themselves. Instead, it alters the relationship between the predictors and the response, which may not solve issues stemming from high correlations among the predictors.

In contrast, removing highly correlated predictors from the model eliminates the overlap in information between them, thereby reducing multicollinearity. Using principal component analysis creates new uncorrelated predictors by combining the correlated ones, which also mitigates the effects of multicollinearity. Transforming the response variable using a linear function, while it might adjust the response without impacting predictors, does not inherently fix the multicollinearity issue but could provide a clearer relationship if the predictors are still problematic.