Journal
STATISTICAL PAPERS
Volume 58, Issue 4, Pages 987-1008Publisher
SPRINGER
DOI: 10.1007/s00362-015-0735-6
Keywords
Kernel method; Relative error; Non-parametric estimation; Associated variable
Categories
Funding
- Campus France (France)
- Agence Thmatique de Recherche en Sciences et Technologie
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Let , be a -valued measurable strictly stationary spatial process. We consider the problem of estimating the regression function of given . We construct an alternative kernel estimate of the regression function based on the minimization of the mean squared relative error. Under some general mixing assumptions, the almost complete consistency and the asymptotic normality of this estimator are obtained. Its finite-sample performance is compared with a standard kernel regression estimator via a Monte Carlo study and real data example.
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