scholarly journals Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification

Author(s):  
Luping Zhou ◽  
Lei Wang ◽  
Philip Ogunbona
2021 ◽  
Vol 69 ◽  
pp. 102940
Author(s):  
Qizhong Zhang ◽  
Bin Guo ◽  
Wanzeng Kong ◽  
Xugang Xi ◽  
Yizhi Zhou ◽  
...  

2015 ◽  
Vol 50 (6) ◽  
pp. 1415-1441 ◽  
Author(s):  
Shingo Goto ◽  
Yan Xu

AbstractIn portfolio risk minimization, the inverse covariance matrix prescribes the hedge trades in which a stock is hedged by all the other stocks in the portfolio. In practice with finite samples, however, multicollinearity makes the hedge trades too unstable and unreliable. By shrinking trade sizes and reducing the number of stocks in each hedge trade, we propose a “sparse” estimator of the inverse covariance matrix. Comparing favorably with other methods (equal weighting, shrunk covariance matrix, industry factor model, nonnegativity constraints), a portfolio formed on the proposed estimator achieves significant out-of-sample risk reduction and improves certainty equivalent returns after transaction costs.


Stress ◽  
2020 ◽  
pp. 1-9
Author(s):  
María Banqueri ◽  
Alba Gutiérrez-Menéndez ◽  
Marta Méndez ◽  
Nélida M. Conejo ◽  
Jorge L. Arias

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