MML Invariant Linear Regression

Authors:
Daniel F. Schmidt;Enes Makalic
Affiliations:
Centre for MEGA Epidemiology, The University of Melbourne, Carlton, Australia 3053;Centre for MEGA Epidemiology, The University of Melbourne, Carlton, Australia 3053
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

Citing 5
Cited 1

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Abstract

This paper derives two new information theoretic linear regression criteria based on the minimum message length principle. Both criteria are invariant to full rank affine transformations of the design matrix and yield estimates that are minimax with respect to squared error loss. The new criteria are compared against state of the art information theoretic model selection criteria on both real and synthetic data and show good performance in all cases.