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locfit.robust

Robust Local Regression

locfit.robust

Usage:


locfit.robust(x, y, ..., iter=3)
locfit(y~x, lfproc=locfit.robust)
locfit.robust(y~x, ..., iter=3)

Description:

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.

Arguments:

x: Either a locfit model formula or a numeric vector of the predictor variable.
y: If x is numeric, y gives the response variable.
...: Other arguments to locfit.raw
iter: Number of iterations to perform

Value:

"locfit" object.