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I am looking for an efficient implementation of local regression (LOESS) and local likelihood methods such as local logistic regression (local likelihood methods are discussed, for example, in section 6.5 of Elements of Statistical Learning by Hastie et. al.).
I would prefer a C++ or Python implementation, but pointers to R (where I know that LOESS is implemented, but I can't find a local likelihood method) or Java would also be appreciated.
Many thanks!
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Local regression is used to model a relation between a predictor variable and re- sponse variable. To keep things simple we will consider the fixed design model. but assume that locally it can be well approximated with a member of a simple class of parametric function, e.g. a constant or straight line.
Local Linear Regression (LLR) is a nonparametric regression model applied in the modeling phase of Response Surface Methodology (RSM). LLR does not make reference to any fixed parametric model. Hence, LLR is flexible and can capture local trends in the data that might be too complicated for the OLS.
Loess regression is a nonparametric technique that uses local weighted regression to fit a smooth curve through points in a scatter plot. Loess curves are can reveal trends and cycles in data that might be difficult to model with a parametric curve.
Menu location: Analysis_LOESS. This is a method for fitting a smooth curve between two variables, or fitting a smooth surface between an outcome and up to four predictor variables. The procedure originated as LOWESS (LOcally WEighted Scatter-plot Smoother).
In R there are the 'locfit' and 'mgcv' packages that I would suggest do forms of local regression. I believe the locfit package is simply a syntactic bridge to an underlying C package. (But not C++.)
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