Mathematical and statistical functions for the Epanechnikov kernel defined by the pdf, $$f(x) = \frac{3}{4}(1-x^2)$$ over the support \(x \in (-1,1)\).
The quantile function is omitted as no closed form analytic expressions could be found, decorate with FunctionImputation for numeric results.
Other kernels:
Cosine,
LogisticKernel,
NormalKernel,
Quartic,
Sigmoid,
Silverman,
TriangularKernel,
Tricube,
Triweight,
UniformKernel
distr6::Distribution -> distr6::Kernel -> Epanechnikov
nameFull name of distribution.
short_nameShort name of distribution for printing.
descriptionBrief description of the distribution.
Inherited methods
distr6::Distribution$cdf()distr6::Distribution$confidence()distr6::Distribution$correlation()distr6::Distribution$getParameterValue()distr6::Distribution$iqr()distr6::Distribution$liesInSupport()distr6::Distribution$liesInType()distr6::Distribution$parameters()distr6::Distribution$pdf()distr6::Distribution$prec()distr6::Distribution$print()distr6::Distribution$quantile()distr6::Distribution$rand()distr6::Distribution$setParameterValue()distr6::Distribution$stdev()distr6::Distribution$strprint()distr6::Distribution$summary()distr6::Distribution$workingSupport()distr6::Kernel$initialize()distr6::Kernel$mean()distr6::Kernel$median()distr6::Kernel$mode()distr6::Kernel$skewness()
pdfSquared2Norm()The squared 2-norm of the pdf is defined by $$\int_a^b (f_X(u))^2 du$$ where X is the Distribution, \(f_X\) is its pdf and \(a, b\) are the distribution support limits.
cdfSquared2Norm()The squared 2-norm of the cdf is defined by $$\int_a^b (F_X(u))^2 du$$ where X is the Distribution, \(F_X\) is its pdf and \(a, b\) are the distribution support limits.
variance()The variance of a distribution is defined by the formula $$var_X = E[X^2] - E[X]^2$$ where \(E_X\) is the expectation of distribution X. If the distribution is multivariate the covariance matrix is returned.