Sparse Laplacian Shrinkage with the Graphical Lasso Estimator for Regression Problems
نویسندگان
چکیده
منابع مشابه
The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression.
We propose a new penalized method for variable selection and estimation that explicitly incorporates the correlation patterns among predictors. This method is based on a combination of the minimax concave penalty and Laplacian quadratic associated with a graph as the penalty function. We call it the sparse Laplacian shrinkage (SLS) method. The SLS uses the minimax concave penalty for encouragin...
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ژورنال
عنوان ژورنال: TEST
سال: 2021
ISSN: 1133-0686,1863-8260
DOI: 10.1007/s11749-021-00779-7