Choice of B-splines with free parameters in the flexible discriminant analysis context

Authors:
Christelle Reynès;Robert Sabatier;Nicolas Molinari
Affiliations:
Laboratoire de Physique Moléculaire et Structurale, Faculté de Pharmacie, 15 av. Ch. Flahault, BP 14491, 34093 Montpellier Cedex 5, France;Laboratoire de Physique Moléculaire et Structurale, Faculté de Pharmacie, 15 av. Ch. Flahault, BP 14491, 34093 Montpellier Cedex 5, France;Laboratoire de Biostatistique, Institut Universitaire de Recherche Clinique, 641 av. G. Giraud, 34093 Montpellier, France
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 5
Cited 0

Generalized principal component analysis with respect to instrumental variables via univariate spline transformations

Computational Statistics & Data Analysis
ADE-4: a multivariate analysis and graphical display software

Statistics and Computing
Bayesian estimation of free-knot splines using reversible jumps

Computational Statistics & Data Analysis
Generalized structured additive regression based on Bayesian P-splines

Computational Statistics & Data Analysis
Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Flexible discriminant analysis (FDA) is a general methodology which aims at providing tools for multigroup non linear classification. It consists in a nonparametric version of discriminant analysis by replacing linear regression by any nonparametric regression method. A new option for FDA, consisting in a nonparametric regression method based on B-spline functions, will be introduced. The relevance of the transformation (hence the discrimination) depends on the parameters defining the spline functions: degree, number and location of the knots for each continuous variable. This method called FDA-FKBS (Free Knot B-Splines) allows to determine all these parameters without the necessity of many prior parameters. It is inspired by Reversible Jumps Monte Carlo Markov Chains but the objective function is different and the Bayesian aspect is put aside.