Functional regression modeling via regularized Gaussian basis expansions

Author:

Araki Yuko,Konishi Sadanori,Kawano Shuichi,Matsui Hidetoshi

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Reference20 articles.

1. Ando T., Imoto S., Konishi S. (2001). Estimating nonlinear regression models based on Gaussian basis function networks (in Japanese). Japanese Journal of Applied Statistics 30: 19–35

2. Ando, T., Konishi, S., Imoto, S. (2005). Nonlinear regression modeling via regularized Gaussian basis function networks. Journal of Statistical Planning and Inference (in press).

3. Araki, Y., Konishi, S. (2006). Functional supervised and unsupervised classification of gene expression data. In: Proceedings in computational statistics 2006, pp. 1105–1112. Physica-Verlag/Springer.

4. Craven P., Wahba G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik 31: 377–403

5. Efron B. (1979). Bootstrap methods: another look at the Jackknife. The Annals of Statistics 7: 1–26

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