期刊
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
卷 90, 期 3, 页码 443-464出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2019.1687701
关键词
LDA; modified Cholesky decomposition; precision matrix; sparsity; order of variables
资金
- Science Education Foundation of Liaoning Province [LN2019Q21]
- National Natural Science Foundation of China [71531004]
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 a sparse precision matrix estimation by addressing the variable order issue in the modified Cholesky decomposition. The 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.
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