Estimates the optimal bandwidth for the maximum kernel likelihood estimator using a Gaussian kernel for a given dataset using the bootstrap.
opt.bw(data, bws=c(sd(data),4*sd(data)), B=1000, gridsize=2^14)The estimated optimal bandwidth.
the data for which the optimal bandwidth should be found.
a vector with the upper and lower bound for the bandwidth.
number of resamples used to estimate the mean squared error with 1000 as the default.
the number of points at which the kernel density estimator is to be evaluated with \(2^{14}\) as the default.
Thomas Jaki
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.
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.
mkle
data(state)
opt.bw(state$CRIME,B=10)
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