Local Bandwidth Selection for Kernel Estimates

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k-NN Regression Adapts to Local Intrinsic Dimension (2011, methodology): “[16, 17]), and our method for choosing k might yield insights into bandwidth selection, since k-NN and kernel regression methods only differ in their notion of neighborhood of a query x.”
Uniform in bandwidth consistency of kernel-type function estimators (2005, background): “he optimal ˆh n(x) in terms of asymptotic mean square error for estimating for msatisﬁes (1.17) with hn =n−1/5. The literature on data-driven bandwidth selection is extensive. We cite, for instance, [[2, 17, 21, 22, 23, 27]]. For further references and methods consult [18], Chapter 7 of [10], [7] an”
Optimal Bandwidth Selection for Kernel Density Functionals Estimation (2015, methodology): “Müller [19] and Staniswalis [20] employed the idea in the kernel regression.”
Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors (1997, background): “Müller and Stadtmüller (1987), Staniswalis (1989) and Brockmann, Gasser and Hermann (1993) propose kernel estimation with locally variable bandwidth selectors.”