desp.cv: Selection of the tuning parameters of desp by v-fold cross-validation
Description
This function returns the precision matrix and the expectation associated to the data matrix X using desp and choosing the tuning parameters $lambda$ and $gamma$ by v-fold cross-validation that uses a robust loss function. The expression of the loss function is provided in the companion vignette.
Usage
desp.cv(X, v, lambda.range, gamma.range, settings=NULL)
Arguments
lambda.range
The range of the penalization parameter $lambda$ that encourages robustness.
gamma.range
The range of the penalization parameter $gamma$ that promotes sparsity.
settings
A list including all the parameters needed for the estimation. Please refer to the documentation of the function desp to get more details. Value
desp.cv returns an object with S3 class "desp.cv" containing the estimated parameters along with the selected values of the tuning parameters, with components: