NONLINEAR REGRESSION MODELING VIA REGULARIZED GAUSSIAN BASIS FUNCTIONS

• Kawano Shuichi

  Graduate School of Mathematics, Kyushu University

• Konishi Sadanori

  Graduate School of Mathematics, Kyushu University

Bibliographic Information

Other Title

• Nonlinear regression modeling via regularized Gaussian basis function

Abstract

Nonlinear regression modeling based on basis expansions has been widely used to explore data with complex structure. There are various types of basis functions to capture complex nonlinear phenomena. In this paper we introduce nonlinear regression models with Gaussian basis functions, for which new Gaussian bases are constructed, taking advantages of $ B $-spline bases. In order to choose adjusted parameters, we derive model selection and evaluation criteria from information-theoretic and Bayesian viewpoints. Monte Carlo simulations and real data analysis show that our proposed modeling strategy performs well in various situations.

Journal

• Bulletin of informatics and cybernetics

  Bulletin of informatics and cybernetics 39 83-96, 2007-12

  Research Association of Statistical Sciences

Keywords

• Basis expansion
• Bayes approach
• Information criterion
• Nonlinear regression
• Regularization

Details

• CRID: 1390009224849128192
• NII Article ID: 120001944234
• NII Book ID: AA10634475
• DOI: 10.5109/16776
• ISSN: 2435743X
• ISSN: 0286522X
• HANDLE: 2324/16776
• Text Lang: en
• Data Source:
  • JaLC
  • IRDB
  • Crossref
  • CiNii Articles
• Abstract License Flag: Allowed