by Hidetoshi Matsui, Yuko Araki and Sadanori Konishi

Journal of Data Science, v.6, no.3, 313-331

Abstract

We propose functional multivariate regression modeling, using Gaussian basis functions along with the technique of regularization. In order to evaluate the model estimated by the regularization method, we derive model selection criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations are conducted to investigate the efficiency of the proposed model. We also apply our modeling strategy to the analysis of spectrometric data.

Homepage | Table of Contents | Full Text of This Article