We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example. © 2002 Elsevier B.V. All rights reserved.
Beran, J., Ghosh, S., & Sibbertsen, P. (2003). Nonparametric M-estimation with long-memory errors. Journal of Statistical Planning and Inference, 117(2), 199–205. https://doi.org/10.1016/S0378-3758(02)00391-9