3.8.7. statsmodels.nonparametric.kernels

Module of kernels that are able to handle continuous as well as categorical variables (both ordered and unordered).

This is a slight deviation from the current approach in statsmodels.nonparametric.kernels where each kernel is a class object.

Having kernel functions rather than classes makes extension to a multivariate kernel density estimation much easier.

NOTE: As it is, this module does not interact with the existing API

3.8.7.1. Functions

aitchison_aitken(h, Xi, x[, num_levels])	The Aitchison-Aitken kernel, used for unordered discrete random variables.
aitchison_aitken_cdf(h, Xi, x_u)
aitchison_aitken_convolution(h, Xi, Xj)
aitchison_aitken_reg(h, Xi, x)	A version for the Aitchison-Aitken kernel for nonparametric regression.
d_gaussian(h, Xi, x)
gaussian(h, Xi, x)	Gaussian Kernel for continuous variables Parameters ———- h : 1-D ndarray, shape (K,) The bandwidths used to estimate the value of the kernel function.
gaussian_cdf(h, Xi, x)
gaussian_convolution(h, Xi, x)	Calculates the Gaussian Convolution Kernel
wang_ryzin(h, Xi, x)	The Wang-Ryzin kernel, used for ordered discrete random variables.
wang_ryzin_cdf(h, Xi, x_u)
wang_ryzin_convolution(h, Xi, Xj)
wang_ryzin_reg(h, Xi, x)	A version for the Wang-Ryzin kernel for nonparametric regression.