Local regression

Summary

Local regression estimates a regression function $m(x) = E[Y \mid X = x]$ by fitting a low-degree polynomial near each point $x$. The fit at $x$ is a weighted least squares fit. Data points close to $x$ get large weights, and distant points get small or zero weights. It needs no global formula for $m$, so it is a basic tool of nonparametric statistics. The methods LOESS and LOWESS are popular versions of it.