Nonparametric M-estimation with long-memory errors

  • Beran J
  • Ghosh S
  • Sibbertsen P
N/ACitations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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.

Cite

CITATION STYLE

APA

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

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free