Abstract
We address estimation of a deterministic function μ, that is the mean of a spatial process y(s) in a nonparametric regression context. Here s denotes a spatial coordinate in \({R}_+^2.\) Given k = n 2 observations, the aim is to estimate μ assuming that y has finite variance, and that the regression errors \(\epsilon (\mathbf {s}) = y(\mathbf {s}) - {E}\left \{ y(\mathbf {s})\right \}\) are Gaussian subordinated.
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Ghosh, S. (2018). On Kernel Smoothing with Gaussian Subordinated Spatial Data. In: Bertail, P., Blanke, D., Cornillon, PA., Matzner-Løber, E. (eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics & Statistics, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-96941-1_19
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DOI: https://doi.org/10.1007/978-3-319-96941-1_19
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