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

Computes different confidence intervals for the maximum kernel likelihood estimator for a given dataset and bandwidth.

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`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.

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