A new Bayesian wavelet thresholding estimator of nonparametric regression

M. Afshari, F. Lak and B. Gholizadeh

Journal of Applied Statistics, 2017, vol. 44, issue 4, 649-666

Abstract: The methods of estimation of nonparametric regression function are quite common in statistical application. In this paper, the new Bayesian wavelet thresholding estimation is considered. The new mixture prior distributions for the estimation of nonparametric regression function by applying wavelet transformation are investigated. The reversible jump algorithm to obtain the appropriate prior distributions and value of thresholding is used. The performance of the proposed estimator is assessed with simulated data from well-known test functions by comparing the convergence rate of the proposed estimator with respect to another by evaluating the average mean square error and standard deviations. Finally by applying the developed method, density function of galaxy data is estimated.

Date: 2017
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DOI: 10.1080/02664763.2016.1182130

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