Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation
Tuning of the bandwidth h of the kernel using the maximum likelihood cross validation.
mkde.tune( x, low = 0.1, up = 3, s = cov(x) )
x |
A matrix with Euclidean (continuous) data. |
low |
The minimum value to search for the optimal bandwidth value. |
up |
The maximum value to search for the optimal bandwidth value. |
s |
A covariance matrix. By default it is equal to the covariance matrix of the data, but can change to a robust covariance matrix, MCD for example. |
Maximum likelihood cross validation is applied in order to choose the optimal value of the bandwidth parameter. No plot is produced.
A list including:
hopt |
The optimal bandwidth value. |
maximum |
The value of the pseudo-log-likelihood at that given bandwidth value. |
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>
Arsalane Chouaib Guidoum (2015). Kernel Estimator and Bandwidth Selection for Density and its Derivatives. The kedd R package. http://cran.r-project.org/web/packages/kedd/vignettes/kedd.pdf
M.P. Wand and M.C. Jones (1995). Kernel smoothing, pages 91-92.
library(MASS) mkde.tune(as.matrix(iris[, 1:4]), c(0.1, 3) )
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