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Title: Geodesic least squares regression on information manifolds

We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply this to scaling laws in magnetic confinement fusion.
Authors:
 [1]
  1. Department of Applied Physics, Ghent University, Ghent, Belgium and Laboratory for Plasma Physics, Royal Military Academy, Brussels (Belgium)
Publication Date:
OSTI Identifier:
22390760
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1636; Journal Issue: 1; Conference: MaxEnt 2013: 33. International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Canberra, ACT (Australia), 15-20 Dec 2013; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; DEFECTS; DISTANCE; GAUSS FUNCTION; GEODESICS; INFORMATION; LEAST SQUARE FIT; MAGNETIC CONFINEMENT; MATHEMATICAL MANIFOLDS; MATHEMATICAL MODELS; SCALING LAWS