6.5.6.2.6. statsmodels.sandbox.nonparametric.kernels.Gaussian¶
-
class
statsmodels.sandbox.nonparametric.kernels.
Gaussian
(h=1.0)[source]¶ Gaussian (Normal) Kernel
K(u) = 1 / (sqrt(2*pi)) exp(-0.5 u**2)
6.5.6.2.6.1. Methods¶
__init__ ([h]) |
|
density (xs, x) |
Returns the kernel density estimate for point x based on x-values |
density_confint (density, nobs[, alpha]) |
approximate pointwise confidence interval for kernel density |
density_var (density, nobs) |
approximate pointwise variance for kernel density |
geth () |
Getter for kernel bandwidth, h |
in_domain (xs, ys, x) |
Returns the filtered (xs, ys) based on the Kernel domain centred on x |
moments (n) |
|
seth (value) |
Setter for kernel bandwidth, h |
smooth (xs, ys, x) |
Returns the kernel smoothing estimate for point x based on x-values xs and y-values ys. |
smoothconf (xs, ys, x[, alpha]) |
Returns the kernel smoothing estimate with confidence 1sigma bounds |
smoothvar (xs, ys, x) |
Returns the kernel smoothing estimate of the variance at point x. |
weight (x) |
This returns the normalised weight at distance x |
6.5.6.2.6.2. Attributes¶
L2Norm |
Returns the integral of the square of the kernal from -inf to inf |
h |
Kernel Bandwidth |
kernel_var |
Returns the second moment of the kernel |
norm_const |
Normalising constant for kernel (integral from -inf to inf) |
normal_reference_constant |
Constant used for silverman normal reference asymtotic bandwidth calculation. |