  In regression analysis for deriving scaling laws that occur in various
scientific disciplines, usually standard regression methods have been applied,
of which ordinary least squares (OLS) is the most popular. In many situations,
the assumptions underlying OLS are not fulfilled, and several other approaches
have been proposed. However, most techniques address only part of the
shortcomings of OLS. We here discuss a new and more general regression method,
which we call geodesic least squares regression (GLS). The method is based on
minimization of the Rao geodesic distance on a probabilistic manifold. For the
case of a power law, we demonstrate the robustness of the method on synthetic
data in the presence of significant uncertainty on both the data and the
regression model. We then show good performance of the method in an application
to a scaling law in magnetic confinement fusion.
