A Bayesian wavelet estimation method for estimating parameters of a stationary I(d) process is represented as an useful alternative to the existing frequentist wavelet estimation methods. The effectiveness of the proposed method is demonstrated through Monte Carlo simulations. The sampling from the posterior distribution is through the Markov Chain Monte Carlo (MCMC) easily implemented in the WinBUGS software package.
"Bayesian Wavelet Estimation Of Long Memory Parameter,"
Journal of Modern Applied Statistical Methods:
1, Article 15.
Available at: http://digitalcommons.wayne.edu/jmasm/vol4/iss1/15