bw.logCV: Optimal CV BW estimation for strictly positive distributions.

In logKDE: Computing Log-Transformed Kernel Density Estimates for Positive Data

Description

Computes least squares cross-validation (CV) bandwidth (BW) for log domain KDE.

Usage

bw.logCV(x, grid = 21, NB = 512)

Arguments

x	numeric vector of the data. Must be strictly positive, will be log transformed during estimation.
grid	number of points used for BW selection CV grid.
NB	number of points at which to estimate the KDE at during the CV loop.

Value

bw the optimal least squares CV bandwidth.

References

Silverman, B. W. (1986). Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability. 26.

Stone, C. J. (1984). An asymptotically optimal window selection rule for kernel density estimates. The Annals of Statistics, 12(4), 1285-1297.

Examples

bw.logCV(rchisq(100,10), grid=21, NB=512)

logKDE documentation built on May 1, 2019, 8:40 p.m.