Standard techniques for selecting the bandwidth of a kernel estimator from the data in a nonparametric regression model perform badly when the errors are correlated. In this paper we propose a ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of Nadaraya-Watson kernel regression using the C# language. NW kernel regression is simple to implement and is ...

Average derivative functionals of regression are proposed for nonparametric model selection and diagnostics. The functionals are of the integral type, which under certain conditions allows their ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...