Description Usage Arguments Details Value Author(s) References See Also Examples
Classical univariate integrated kernel density estimator
1 | IntKde(xin, xout, h, kfun)
|
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 3.572*σ*n^{-1/3} according to Bowman et al.(1998). |
kfun |
The kernel to use in the distribution function estimate. |
It implements the classical density integrated kernel estimator.
Let X_1,X_2,…, 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}∑_{i=1}^n K≤ft \{ (x-X_i)h^{-1}\right \}
where K is an integrated kernel, and h > 0 is a smoothing parameter called the bandwidth.
Returns a vector with the estimate of the distribution function at the user specified grid points.
Dimitrios Bagkavos and Lucia Gamez Gallardo
R implementation and documentation: Dimitrios Bagkavos <dimitrios.bagkavos@gmail.com>, Lucia Gamez Gallardo <gamezgallardolucia@gmail.com>
bw.nrd
, bw.nrd0
, bw.ucv
, bw.bcv
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