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

DOI10.1016/j.csda.2005.11.018zbMath1157.62444OpenAlexW2046589626MaRDI QIDQ1010528

Nicolas Molinari, Christelle Reynès, Robert Sabatier

Publication date: 6 April 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2005.11.018

zbMATH Keywords

B-splinesdiscriminationMonte Carlo Markov chainreversible jumpflexible discriminant analysis

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Numerical analysis or methods applied to Markov chains (65C40)

Uses Software

• Matlab
• R
• ADE-4

Cites Work

• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Multivariate adaptive regression splines
• The `lethargy' theorem - a property of approximation by \(\gamma\)- polynomials
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• A practical guide to splines
• Generalized principal component analysis with respect to instrumental variables via univariate spline transformations
• Bayesian estimation of free-knot splines using reversible jumps.
• Spline adaptation in extended linear models
• Generalized structured additive regression based on Bayesian P-splines
• Mining the customer credit using classification and regression tree and multivariate adaptive regression splines
• On Polya frequency functions. IV: The fundamental spline functions and their limits
• Bayesian curve-fitting with free-knot splines
• Approximation to Data by Splines with Free Knots
• Flexible Discriminant Analysis by Optimal Scoring
• Polychotomous Regression
• Additive Splines for Partial Least Squares Regression
• The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
• The elements of statistical learning. Data mining, inference, and prediction

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