What are Nonparametric Methods in the StatsModels library?

This recipe describes what Nonparametric Methods are in StatsModels

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Recipe Objective - What are Nonparametric Methods in the StatsModels library?

Nonparametric methods include kernel density estimation for univariate and multivariate data, kernel regression, and smoothing (minimum) of locally weighted scatter plots.

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The sandbox.nonparametric contains additional functions that are in progress or have not yet been unit tested. We plan to include smoother tools specifically for methods of nonparametric density estimators, nonparametric models, and other parts of statistical models based on the kernel or orthogonal polynomials.

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Table of Contents

Kernel density estimation

Kernel density estimation (KDE) is divided into univariate estimation and multivariate estimation and is implemented in entirely different ways.

Kernel Regression

Kernel regression (provided by KernelReg) is based on the same product kernel approach as KDE Multivariate, so it uses the same functions (mixed data, cross-validated bandwidth estimation, kernel) as described above for KDE Multivariate. Hold. Censored regression is provided by KernelCensoredReg.

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