Kernel regression

Summary

Kernel regression estimates the regression function $m(x) = E[Y \mid X = x]$ without assuming a parametric form for it. The estimate at $x$ is a weighted average of the observed responses, with weights given by a kernel that favours observations with $X_i$ near $x$. Its standard form is the Nadaraya–Watson estimator, a regression analogue of kernel density estimation.