Edist.meas: Compute the Expected Distance in Measure (EDM) between a...

Description Usage Arguments Value References

Description

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

Usage

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Edist.meas(n = 100, hmin = 0.05, hmax = 1, byh = hmin, mus = 0,
  sigmas = 1, props = 1, plot = TRUE, B = 100, verbose = TRUE)

Arguments

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 EDM as a function of bandwidth values in the grid is displayed along with the single DM for each Monte Carlo sample.

B

the number of Monte Carlo samples.

verbose

if TRUE, the computational progression is displayed.

Value

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

References

Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science 30(4): 518-532.


AlessandroCasa/BsMc documentation built on Oct. 30, 2019, 4:49 a.m.