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Kernel smoothing M.C. Jones, M.P. Wand
Publisher: Chapman & Hall

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. (and can over-smoothing be tuned/quantified?) I don't know if smoothing along one direction independently of the other makes sense, or if it's better to smooth in 2D. The technique involves using what is called “kernel smoothing”. Or I need something like radial basis function (with spline-tension kernel) / kernel smoothing interpolation method? Interpolation (or any other tool) to make my network finer and only then to interpolate my values? Kernel Smoothing (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) book download. Little or no training is required for operation of the kernel smoother. In the method of "kernel smoothing density," i.e. A Monte-Carlo study investigates the uniform kernel smoothed estimator and its variance estimator. Instead of using those methods and just choosing a bin size, I decided to smooth things out, and create an average histogram. The estimated function is smooth, and the level of smoothness is set by a single parameter. The kernel smoothing technique is utilized in estimating the hazard rate function.