Nonparametric regression estimation for random fields in a fixed-design

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We investigate the nonparametric estimation for regression in a fixed-design setting when the errors are given by a field of dependent random variables. Sufficient conditions for kernel estimators to converge uniformly are obtained. These estimators can attain the optimal rates of uniform convergence and the results apply to a large class of random fields which contains martingale-difference random fields and mixing random fields.
Accepté pour publication dans la revue "Statistical Inference for Stochastic Processes"

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Statistics Theory, Probability, 60G60; 62G08

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http://salesiana.dossiersoluciones.com/handle/123456789/130694

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0502091
 http://arxiv.org/abs/math/0502091

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arXiv