opt.bw: Optimal bandwidth for the maximum kernel likelihood estimator

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

Estimates the optimal bandwidth for the maximum kernel likelihood estimator using a Gaussian kernel for a given dataset using the bootstrap.

Usage

1
opt.bw(data, bws=c(sd(data),4*sd(data)), B=1000, gridsize=2^14)

Arguments

data

the data for which the optimal bandwidth should be found.

bws

a vector with the upper and lower bound for the bandwidth.

B

number of resamples used to estimate the mean squared error with 1000 as the default.

gridsize

the number of points at which the kernel density estimator is to be evaluated with 2^{14} as the default.

Details

The bandwidth considered fall between one and 4 standard deviations. In addition the mse of the mkle for a bandwidth of zero will also be included.

The estimation of the optimal bandwidth might take several minutes depending on the number of bootstrap resamples and the gridsize used.

Value

The estimated optimal bandwidth.

Note

The optimize is used for the optimization.

Author(s)

Thomas Jaki

References

Jaki T., West R. W. (2008) Maximum kernel likelihood estimation. Submitted to Journal of Computational and Graphical Statistics Vol. 17(No 4), 976-993.

Davison, A. C. and Hinkley, D. V. (1997), Bootstrap Methods and their Applications, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press.

See Also

mkle

Examples

1
2
data(state)
opt.bw(state$CRIME,B=100)

MKLE documentation built on May 29, 2017, 9:31 a.m.