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Finally, as discussed above, LOESS is a computationally intensive method (with the exception of evenly spaced data, where the regression can then be phrased as a non-causal [[finite impulse response]] filter). LOESS is also prone to the effects of outliers in the data set, like other least squares methods. There is an iterative, [[robust statistics|robust]] version of LOESS [Cleveland (1979)] that can be used to reduce LOESS' sensitivity to [[outliers]], but too many extreme outliers can still overcome even the robust method.
==Further
Books substantially covering local regression and extensions:
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