locfit.robust implements a robust local regression where
outliers are iteratively identified and downweighted, similarly
to the lowess method (Cleveland, 1979). The iterations and scale
estimation are performed on a global basis.
The scale estimate is 6 times the median absolute residual, while the robust downweighting uses the bisquare function. These are performed in the S code so easily changed.
This can be interpreted as an extension of M estimation to local
regression. An alternative extension (implemented in locfit via
family="qrgauss") performs the iteration and scale estimation
on a local basis.
locfit.robust(x, y, weights, ..., iter=3)"locfit" object.
Either a locfit model formula or a numeric vector
of the predictor variable.
If x is numeric, y gives the response variable.
weights to use in the fitting.
Other arguments to locfit.raw.
Number of iterations to perform
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assn. 74, 829-836.
locfit,
locfit.raw