Manual: Nonparametric Methods nonparametric

Nonparametric Methods nonparametric

This section collects various methods in nonparametric statistics. This includes kernel density estimation for univariate and multivariate data, kernel regression and locally weighted scatterplot smoothing (lowess).

sandbox.nonparametric contains additional functions that are work in progress or don’t have unit tests yet. We are planning to include here nonparametric density estimators, especially based on kernel or orthogonal polynomials, smoothers, and tools for nonparametric models and methods in other parts of statsmodels.

Kernel density estimation

The kernel density estimation (KDE) functionality is split between univariate and multivariate estimation, which are implemented in quite different ways.

Univariate estimation (as provided by KDEUnivariate) uses FFT transforms, which makes it quite fast. The