  This paper introduces a novel statistical regression framework that allows
the incorporation of consistency constraints. A linear and nonlinear
(kernel-based) formulation are introduced, and both imply closed-form
analytical solutions. The models exploit all the information from a set of
drivers while being maximally independent of a set of auxiliary, protected
variables. We successfully illustrate the performance in the estimation of
chlorophyll content.
