BAYESIAN NONPARAMETRIC REGRESSION USING WAVELETS
Jean‐François Angers, Mohan Delampady · Sankhya. Series A · 2016
Multi-resolution analysis is used here to derive a wavelet smoother as an estimated regression function for a given set of noisy data. The hierarchical Bayesian approach is employed to model the regression function using a wavelet basis and to perform the subsequent estimations. The Bayesian model selection tool of Bayes factor is used to select the optimal resolution level of the multi-resolution analysis. Error bands are provided as an index of estimation error. The methodology is illustrated with two examples and a simulation study.