Description Usage Arguments Value References
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.
1 2 | Edist.meas(n = 100, hmin = 0.05, hmax = 1, byh = hmin, mus = 0,
sigmas = 1, props = 1, plot = TRUE, B = 100, verbose = 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 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. |
the value of the EDM for each value in the grid.
Chacón, J.E. (2015). A population background for nonparametric density-based clustering. Statistical Science 30(4): 518-532.
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