Nonlinear regression modeling via regularized wavelets and smoothing parameter selection

• Fujii, Toru

  Graduate School of Mathematics, Kyushu University

• Konishi, Sadanori

  Faculty of Mathematics, Kyushu University

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.

Special Issue dedicated to Prof. Fujikoshi

Kyushu University 21st Century COE Program Development of Dynamic Mathematics with High Functionality

Journal

• Journal of multivariate analysis

  Journal of multivariate analysis 97 (9), 2023-2033, 2006-10

  Elsevier

Keywords

• Automatic smoothing parameter selection
• Irregular design points
• Linear shrinkage; Regression modeling
• Wavelets
• 62J02
• 62M10

Details

• CRID: 1050861482659261312
• NII Article ID: 120000981496
• NII Book ID: AA0025295X
• HANDLE: 2324/11855
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • CiNii Articles