A Note on A Bayesian Approach to the Choice of Wavelet Basis Functions at Each Resolution Level

A Note on A Bayesian Approach to the Choice of Wavelet Basis Functions at Each Resolution Level

In recent years wavelet methods have been focused on block shrinkage or thresholding approaches to accounting for the sparseness of the wavelet representation for an unknown function. The block shrinkage or thresholding methods have been developed in both of classical methods and Bayesian methods. In this paper, we propose a Bayesian approach to selecting wavelet basis functions at each resolution level without MCMC procedure. Simulation study and an application are shown.

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