Nonlinear regression modeling via regularized wavelets and smoothing parameter selection

  • Authors:
  • Toru Fujii;Sadanori Konishi

  • Affiliations:
  • Graduate School of Mathematics, Kyushu University, Hakozaki, Higashi-ku, Fukuoka, Japan;Faculty of Mathematics, Kyushu University, Hakozaki, Higashi-ku, Fukuoka, Japan

  • Venue:
  • Journal of Multivariate Analysis - Special issue dedicated to Professor Yasunori Fujikoshi
  • Year:
  • 2006

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Abstract

We introduce regularized wavelet-based methods for nonlinear regression modeling when design points are not equally spaced. A crucial issue in the model building process is a choice of tuning parameters that control the smoothness of a fitted curve. We derive model selection criteria from an information-theoretic and also Bayesian approaches. Monte Carlo simulations are conducted to examine the performance of the proposed wavelet-based modeling technique.