The linearity assumption surrounding a quantitative variable in common
practice may be a reasonable approximation especially when the variable
is confined within a narrow range, but can be inappropriate under some
circumstances when the variable's effect is non-monotonic or tortuous.
As a more flexible and adaptive approach, 3dMSS adopts a principle of
learning from the data in the presence of uncertainty to dissolve the
problematic aspects of conventional polynomial fitting. It offers a powerful
analytical tool for population-level neuroimaging data analysis that involves
one or more quantitative predictors. Check out the examples in the help
document of 3dMSS. More functionality and use examples will be added
soon.

This preprint covers the underlying theory and a neuroimaging application:
[www.biorxiv.org]

Gang



Edited 2 time(s). Last edit at 11/03/2020 09:02AM by Gang.