Description Usage Arguments Value References

Compute the Asymptotic Expected Distance in Measure (AEDM) between a kernel estimator-induced partition and the population one defined by a K-component normal mixture density over a grid of bandwidths.

1 2 | ```
AEdist.meas(n = 100, hmin = 0.05, hmax = 1, byh = hmin, mus = 0,
sigmas = 1, props = 1, plot = TRUE)
``` |

`n` |
sample size of the simulated Monte Carlo samples. |

`hmin` |
lower value for the grid of bandwidths. |

`hmax` |
upper value for the grid of bandwidths. |

`byh` |
increment of the grid sequence |

`mus` |
vector of means of the mixture components. |

`sigmas` |
vector of standard deviations of the mixture components. |

`props` |
vector of mixing proportions of the mixture components. |

`plot` |
if TRUE the curve of the AEDM as a function of bandwidth values in the grid is displayed along with the single DM for each Monte Carlo sample. |

the value of the EDM for each value in the grid.

Casa A., Chacón, J.E. and Menardi, G. (2019). Modal clustering asymptotics with applications to bandwidth selection (https://arxiv.org/pdf/1901.07300.pdf).

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