Dehgression

Dehgression is a term used in some contexts to describe a specific type of regression analysis, particularly when the predictor variables are not strictly independent or when there are concerns about multicollinearity. While not a universally recognized statistical term like "linear regression" or "logistic regression," it suggests an approach where the selection of variables or the interpretation of coefficients is done with a focus on avoiding spurious relationships or over-fitting the model.

The core idea behind dehgression, as implied by its name, might involve a process of "degreasing" or simplifying a model to achieve a more robust and interpretable result. This could manifest in several ways. It might involve careful variable selection, perhaps using techniques that penalize complexity or prioritize variables with stronger theoretical backing. Alternatively, it could refer to a more cautious interpretation of regression coefficients, acknowledging the potential for bias introduced by correlated predictors.

Researchers employing a dehgression approach might be particularly attentive to diagnostic checks for multicollinearity, such as variance inflation factors (VIFs). They might also favor methods like ridge regression or lasso regression, which are designed to handle multicollinearity by introducing a degree of shrinkage to the coefficient estimates. The goal is often to produce a model that generalizes well to new data and provides clearer insights into the relationships between variables, even in the presence of complex interactions or dependencies.