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. © Springer-Verlag Berlin Heidelberg 2009.
Cite
CITATION STYLE
Schmidt, D. F., & Makalic, E. (2009). MML invariant linear regression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5866 LNAI, pp. 312–321). https://doi.org/10.1007/978-3-642-10439-8_32
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
