In order to estimate the “pure” effect of some explanatory variable on the dependent variable, we want to control for as many other effects as possible. That is, we’d like to see how our prediction ...

The application of Cox proportional hazards (CoxPH) models to survival data and the derivation of hazard ratio (HR) are well established. Although nonlinear, tree-based machine learning (ML) models ...

U-shaped nonlinearities If it is suspected that there is a “bend” in the way some explanatory variable affects the dependent variable, it can be useful to introduce the square of that explanatory ...

In the linear regression model $\text {Y}=\beta _ {1}\text {X}_ {1}+\beta _ {2}\text {X}_ {2}+\text {u}$, the coefficients β 1 and β 2 may be estimated by least squares. If the explanatory variable X2 ...

Special Considerations A simple regression model, or equation, consists of four terms. On the left side is the dependent variable. It represents the phenomenon the model seeks to "explain." On the ...

Walter Torous, Rossen Valkanov, Shu Yan, On Predicting Stock Returns with Nearly Integrated Explanatory Variables, The Journal of Business, Vol. 77, No. 4 (October 2004), pp. 937-966 ...

The components of progression as explanatory variables for overall survival in the RECIST database. Authors: Saskia Litière, Elisabeth De Vries, Lesley Seymour, Daniel J. Sargent, Lalitha Shankar, and ...