Wavelet-RKHS-based functional statistical classification
A functional classification methodology, based on the Reproducing Kernel Hilbert Space (RKHS) theory, is proposed for discrimination of gene expression profiles. The parameter function involved in the definition of the functional logistic regression is univocally and consistently estimated, from the minimization of the penalized negative log-likelihood over a RKHS generated by a suitable wavelet basis. An iterative descendent method, the gradient method, is applied for solving the corresponding minimization problem, i.e., for computing the functional estimate. Temporal gene expression data involved in the yeast cell cycle are classified with the wavelet-RKHS-based discrimination methodology considered. A simulation study is developed for testing the performance of this statistical classification methodology in comparison with other statistical discrimination procedures. Copyright Springer-Verlag 2012
Volume (Year): 6 (2012)
Issue (Month): 3 (October)
|Contact details of provider:|| Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634|
|Order Information:||Web: http://link.springer.de/orders.htm|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Yuko Araki & Sadanori Konishi & Shuichi Kawano & Hidetoshi Matsui, 2009. "Functional regression modeling via regularized Gaussian basis expansions," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(4), pages 811-833, December.
- Sadanori Konishi, 2004. "Bayesian information criteria and smoothing parameter selection in radial basis function networks," Biometrika, Biometrika Trust, vol. 91(1), pages 27-43, March.
- Angelini, Claudia & De Canditiis, Daniela & Leblanc, Frédérique, 2003. "Wavelet regression estimation in nonparametric mixed effect models," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 267-291, May.
When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:6:y:2012:i:3:p:201-217. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.