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Sparse covariance thresholding for high-dimensional variable selection

Sparse covariance thresholding for high-dimensional variable selection

In high-dimensions, many variable selection methods, such as the lasso, are often limited by excessive variability and rank deficiency of the sample covariance matrix.Covariance sparsity is a natural phenomenon in high-dimensional applications, such as microarray analysis, image processing, etc., in which a large number of predictors are independent or weakly …