Local Regression - Disadvantages of LOESS

Disadvantages of LOESS

LOESS makes less efficient use of data than other least squares methods. It requires fairly large, densely sampled data sets in order to produce good models. This is not really surprising, however, since LOESS needs good empirical information on the local structure of the process in order to perform the local fitting. In fact, given the results it provides, LOESS could be more efficient overall than other methods like nonlinear least squares. It may simply frontload the costs of an experiment in data collection but then reduce analysis costs.

Another disadvantage of LOESS is the fact that it does not produce a regression function that is easily represented by a mathematical formula. This can make it difficult to transfer the results of an analysis to other people. In order to transfer the regression function to another person, they would need the data set and software for LOESS calculations. In nonlinear regression, on the other hand, it is only necessary to write down a functional form in order to provide estimates of the unknown parameters and the estimated uncertainty. Depending on the application, this could be either a major or a minor drawback to using LOESS.

Finally, as discussed above, LOESS is a computationally intensive method. This is not usually a problem in our current computing environment, however, unless the data sets being used are very large. LOESS is also prone to the effects of outliers in the data set, like other least squares methods. There is an iterative, robust version of LOESS that can be used to reduce LOESS' sensitivity to outliers, but too many extreme outliers can still overcome even the robust method.