Bayesian approach to smoothing parameter selection in spline estimate for regression curve
by Sonia Amroun; Lamia Djerroud; Smail Adjabi
International Journal of Computing Science and Mathematics (IJCSM), Vol. 12, No. 3, 2020

Abstract: Spline functions have proved to be very useful in statistics, in particular, to estimate the nonparametric regression. Many different smoothing parameter selectors for the smoothing spline are proposed in the literature such as cross-validation (CV), generalised cross-validation (GCV). In this article, we propose the Bayesian approach to estimate the smoothing parameter and the variance of the Gaussian error model in the context of the nonparametric regression. We use the Markov chain Monte Carlo (MCMC) method to compute the estimators given by the proposed Bayesian approach. The performance of the Bayesian approach is compared with the classical generalised cross-validation method through simulation and real data.

Online publication date: Fri, 11-Dec-2020

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