Robust Selection

Robust Selection algorithm for estimation of the regularization parameter for Graphical Lasso

Usage

robsel(x, alpha = 0.9, B = 200)

Arguments

x

A n-by-p data matrix

alpha

Prespecified confidence level. Default 0.9

B

Number of bootstrap sample. Default 200

Value

lambda Estimation of the regularization parameter for Graphical Lasso. A vector of lambda will be return if more than 1 value of alpha is provided.

References

P Cisneros-Velarde, A Petersen and S-Y Oh (2020). Distributionally Robust Formulation and Model Selection for the Graphical Lasso. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics.

See also

robsel.glasso for using Graphical Lasso algorithm with estimate regularization parameter lambda from Robust Selection.

Examples

set.seed(17)
library(robsel)
x <-matrix(rnorm(50*20),ncol=20)

#Compute estimation of lambda at confidence level alpha
lambda <- robsel(x = x, alpha = 0.9, B = 200)