An optimal aggregation type classifier

CHOLAQUIDIS, ALEJANDRO; KALEMKERIAN, JUAN; FRAIMAN, RICARDO; LLOP, PAMELA

Contributions in infinite-dimensional statistics and related topics

Societa Editrice Esculapio

Año: 2014; p. 1 - 280

We introduce a nonlinear aggregation type classifier for functional data defined on a separable and complete metric space. The new rule is built up from a collection of M arbitrary training classifiers. If the classifiers are consistent, then so is the aggregation rule. Moreover, asymptotically the aggregation rule behaves as well as the best of the M classifiers. The results of a small simulation are reported both, for high dimensional and functional data.