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• W3014218689 abstract "The modified Cholesky decomposition is commonly used for precision matrix estimation given a specified order of random variables. However, the order of variables is often not available or cannot be pre-determined. In this work, we propose to address the variable order issue in the modified Cholesky decomposition for sparse precision matrix estimation. The key idea is to effectively combine a set of estimates obtained from multiple permutations of variable orders, and to efficiently encourage the sparse structure for the resultant estimate by the thresholding technique on the ensemble Cholesky factor matrix. The consistent property of the proposed estimate is established under some weak regularity conditions. Simulation studies are conducted to evaluate the performance of the proposed method in comparison with several existing approaches. The proposed method is also applied into linear discriminant analysis of real data for classification." @default.
• W3014218689 created "2020-04-10" @default.
• W3014218689 creator A5003650269 @default.
• W3014218689 creator A5003817085 @default.
• W3014218689 date "2017-10-14" @default.
• W3014218689 modified "2023-09-27" @default.
• W3014218689 title "An Improved Modified Cholesky Decomposition Method for Precision Matrix Estimation" @default.
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• W3014218689 doi "https://doi.org/10.48550/arxiv.1710.05163" @default.
• W3014218689 hasPublicationYear "2017" @default.
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