Robust Local Regression
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)
x |
Either a |
y |
If |
weights |
weights to use in the fitting. |
... |
Other arguments to |
iter |
Number of iterations to perform |
"locfit" object.
Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assn. 74, 829-836.
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