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A conventional linear model for functional data involves expressing a response variable Y in terms of the explanatory function X(t), via the model , where a is a scalar, b is an unknown function and is a compact interval. However, in some problems the support of b or X, say, is a proper and unknown subset of , and is a quantity of particular practical interest. Motivated...
In this note we show that, from a conventional viewpoint, there are particularly close parallels between optimal-kernel-choice problems in non-parametric deconvolution, and their better-understood counterparts in density estimation and regression. However, other aspects of these problems are distinctly different, and this property leads us to conclude that “optimal” kernels do not give satisfactory...
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