Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416864 | Computational Statistics & Data Analysis | 2012 | 13 Pages |
Abstract
A natural methodology for discriminating functional data is based on the distances from the observation or its derivatives to group representative functions (usually the mean) or their derivatives. It is proposed to use a combination of these distances for supervised classification. Simulation studies show that this procedure performs very well, resulting in smaller testing classification errors. Applications to real data show that this technique behaves as well as–and in some cases better than–existing supervised classification methods for functions.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Andrés M. Alonso, David Casado, Juan Romo,