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

Find asymptotically optimal bandwidth for Gaussian kernel density estimation by minimizing mean squared error at the first data point.

1 | ```
bw.mse.pdf.asym(x,iter.max=1L,eps=1e-6,start.bw=bw.nrd, verbose=FALSE)
``` |

`x` |
A numeric vector whose density is to be estimated. |

`iter.max` |
The max number of iterations. For an iterative procedure, set this to be greater than 1L. |

`eps` |
A small positive number such that iterations are terminated when the difference between successive bandwidths is below |

`start.bw` |
A function, a character, or a positive number. If being a function or a character, the corresponding function will be called with |

`verbose` |
Logical, when |

The procedure starts from the starting bandwidth specified by `start.bw`

. The new bandwidth will be computed as

*
( {Rf(x_1)} / [N{f''(x_1)}^2] )^{1/5}
*

where `R`

is approximately 0.2821, `N`

is the sample size and `f`

is the density estimate using the current bandwidth.

A numeric positive scalar, giving the final bandwidth.

Long Qu

Parzen, E. (1962). On Estimation of a Probability Density Function and Mode. The Annals of Mathematical Statistics 33: 1065–1076.

Scott, D. W. (1992). Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley, New York.

Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

1 2 3 4 5 6 7 8 9 |

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