Locallyweighted regression (skmisc.loess
)¶
Loess is a procedure for estimating a regression surface by a multivariate smoothing procedure. A linear or quadratic function of the independent variables is fit in a moving fashion that is analogous to how a moving average is computed for a time series.
Compared to approaches that fit global parametric functions, loess substantially increases the domain of surfaces that can be estimated without distortion. However, analogues of the statistical procedures used in parametric function fitting – for example, ANOVA and t intervals – involve statistics whose distributions are well approximated by familiar distributions.

Locallyweighted regression 

Initialization class for loess data inputs 

Initialization class for loess fitting parameters 

Initialization class for loess control parameters 

Class of a loess fit outputs 

Class for loess prediction results 

Pointwise confidence intervals of a loesspredicted object 

Analysis of variance for two loess objects 
Source¶
The original source code was written by William S. Cleveland, Eric Grosse and MingJen Shyu. It is available at http://www.netlib.org/a/dloess. It was initially adapted to for use in Scipy by Pierre GF GerardMarchant.
For more see references [1] [2] and [3].
W. S. Cleveland, E. Grosse, and M. J. Shyu. Local Regression Models. In J. M. Chambers and T. Hastie, editors, Statistical Models in S, pages 309–376. Chapman and Hall, New York, 1992.
W. S. Cleveland, S. J. Devlin, and E. Grosse. Regression by Local Fitting: Methods, Properties, and Computing. Journal of Econometrics, 37: pp. 87–114. 1988.
W. S. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots. Journal of the American Statistical Association, 74: pp. 829–836. 1979.