IntKde {asymmetry.measures} | R Documentation |
Integrated Kernel density estimator
Description
Classical univariate integrated kernel density estimator
Usage
IntKde(xin, xout, h, kfun)
Arguments
xin |
A vector of data points - the available sample size. |
xout |
grid points where the distribution function will be estimated. |
h |
The bandwidth parameter. Defaults to |
kfun |
The kernel to use in the distribution function estimate. |
Details
It implements the classical density integrated kernel estimator.
Let X_1,X_2,\dots, X_n
be a univariate independent and identically distributed sample drawn from some unknown distribution function F
. Its kernel density estimator is
\hat{F}(x)= n^{-1}\sum_{i=1}^n K\left \{ (x-X_i)h^{-1}\right \}
where K
is an integrated kernel, and h > 0
is a smoothing parameter called the bandwidth.
Value
Returns a vector with the estimate of the distribution function at the user specified grid points.
Author(s)
Dimitrios Bagkavos and Lucia Gamez Gallardo
R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com>, Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>
References
See Also
bw.nrd
, bw.nrd0
, bw.ucv
, bw.bcv
Examples
x.in <- rnorm(100)
x.out <- seq(-3.4,3.4,length=60)
kernel <- IntEpanechnikov
dist.est <- IntKde(xin=x.in,xout=x.out,kfun=kernel)
plot(x.out,dist.est, type="l", col="red", main="Kernel c.d.f. estimator")