6.5.6.2.10. statsmodels.sandbox.nonparametric.kernels.Uniform

class statsmodels.sandbox.nonparametric.kernels.Uniform(h=1.0)[source]
__init__(h=1.0)[source]

6.5.6.2.10.1. 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

6.5.6.2.10.2. 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.