6.5.7.1.1. statsmodels.sandbox.nonparametric.smoothers.KernelSmoother

class statsmodels.sandbox.nonparametric.smoothers.KernelSmoother(x, y, Kernel=None)[source]

1D Kernel Density Regression/Kernel Smoother

Requires: x - array_like of x values y - array_like of y values Kernel - Kernel object, Default is Gaussian.

__init__(x, y, Kernel=None)[source]

6.5.7.1.1.1. Methods

__init__(x, y[, Kernel])
conf(x) Returns the fitted curve and 1-sigma upper and lower point-wise confidence.
fit()
predict(x) Returns the kernel smoothed prediction at x
std(x)
var(x)