Regression by Local Fitting: Methods, Properties, and Computational Algorithms

Local regression is a procedure for estimating regression surfaces by the local fitting of linear or quadratic functions of the independent variables in a moving fashion that is analogous to how a moving average is computed for a time series. The advantage of the methodology over the global fitting of parametric functions of the independent variables by least squares, the current paradigm in regression studies, is that a much wider class of regression functions can be estimated without distortion.