Abstract
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.
DOI
10.22237/jmasm/1114906500
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
