A note on wavelet shrinkage in nonparametric regression models with ARFIMA errors

In this paper, we propose a shrinkage wavelet-based method to estimate the signal in a nonparametric regression model with Autoregressive Fractionally Integrated Moving Average (ARFIMA) errors. Monte Carlo experiments indicate that the proposed method is better than widely used wavelet shrinkage methods, and is the best in situations where the noise has very long memory.