Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7548066 | Statistics & Probability Letters | 2018 | 12 Pages |
Abstract
In the present paper we propose recursive general kernel-type estimators for spatial data defined by the stochastic approximation algorithm. We obtain the central limit theorem and strong pointwise convergence rate for the nonparametric recursive general kernel-type estimators under some mild conditions. Finally, we investigate the MISE of the proposed estimators and provide the optimal bandwidth.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Salim Bouzebda, Yousri Slaoui,