The algorithm searches for smoothing parameters on the interval [0, smax]. smax is problem dependent, and the defaults are not consistently appropriate.
n.points
The number of points in [0, smax] at which the algorithm looks for crossing of the threshold c.thresh*sqrt(log(n,10)/n). If the sample characteristic function is highly oscillatory on [0,smax], this may need to be increased.
Kn
Tuning parameter Kn discussed in Politis (2003). Roughly, the distance over which the sample characteristic function must stay below the threshold determined by c.thresh.
c.thresh
The bandwidth is chosen by looking for the first time the sample characteristic function drops below c.thresh*sqrt(log(n,10)/n) and stays below that level for a distance of Kn.
...
Currently unimplemented.
Value
Returns the estimated kernel bandwidth h.
Details
Returns a bandwidth, h, for use with infinite order flat-top kernel density estimates. All frequencies higher than 1/h are downweighted by the kernel. All kernels in this package are scaled to use roughly the same bandwidth. We recommend using this algorithm in conjunction with bwplot.numeric to double check the automated selection.
References
Politis, D. N. (2003). Adaptive bandwidth choice. Journal of Nonparametric Statistics, 15(4-5), 517-533.
x <- rnorm(100)
bwplot(x)
h <- bwadap(x)
plot(iodensity(x, h, kernel="Trap"), type="l")
rug(x)
# Add the truth in redxs <- seq(-3, 3, len=1000)
lines(xs, dnorm(xs), col="red")