Description Usage Arguments Value Author(s) See Also
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.
1 |
X |
The data matrix. |
v |
The number of folds. |
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.cv
returns an object with S3 class "desp.cv" containing the estimated parameters along with the selected values of the tuning parameters, with components:
Omega |
The precision matrix. |
mu |
The expectation vector. |
Theta |
The matrix corresponding to outliers. |
lambda |
The selected lambda. |
gamma |
The selected gamma. |
Arnak Dalalyan and Samuel Balmand.
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