mkle.ci | R Documentation |
Computes different confidence intervals for the maximum kernel likelihood estimator for a given dataset and bandwidth.
mkle.ci(data, bw=2*sd(data), alpha=0.1, kernel=c("gaussian", "epanechnikov",
"rectangular", "triangular", "biweight", "cosine", "optcosine"),
method=c("percentile", "wald","boott"), B=1000, gridsize=2^14)
data |
the data for which the confidence interval should be found. |
bw |
the smoothing bandwidth to be used. |
alpha |
the significance level. |
kernel |
a character string giving the smoothing kernel to be used. This must be one of '"gaussian"', '"rectangular"', '"triangular"', '"epanechnikov"', '"biweight"', '"cosine"' or '"optcosine"', with default '"gaussian"', and may be abbreviated to a unique prefix (single letter). |
method |
a character string giving the type of interval to be used. This must be one of '"percentile"', '"wald"' or '"boott"'. |
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 |
The method can be a vector of strings containing the possible choices.
The bootstrap-t-interval can be very slow for large datasets and a large number of resamples as a two layered resampling is necessary.
A dataframe with the requested intervals.
Thomas Jaki
Jaki T., West R. W. (2008) Maximum kernel likelihood estimation. 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)
mkle.ci(state$CRIME,method=c('wald','percentile'),B=100,gridsize=2^11)
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