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.

See Also:

locfit, locfit.raw

References:

Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assn. 74, 829-836.

Key Words:

smooth

Key Words:

survival
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