A Random Matrix-Theoretic Approach to Handling Singular Covariance Estimates
In many practical situations we would like to estimate the covariance matrix of a set of variables from an insufficient amount of data. More specifically, if we have a set of N independent, identically distributed measurements of an M dimensional random vector the maximum likelihood estimate is the...
मुख्य लेखकों: | Marzetta, T, Tucci, G, Simon, S |
---|---|
स्वरूप: | Journal article |
भाषा: | English |
प्रकाशित: |
2011
|
समान संसाधन
-
Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
द्वारा: Nir Gorelik, और अन्य
प्रकाशित: (2012-01-01) -
Weighted covariance matrix estimation
द्वारा: Yang, Guangren, और अन्य
प्रकाशित: (2020) -
Covariance estimation on matrix manifolds
द्वारा: Musolas Otaño, Antoni M.(Antoni Maria)
प्रकाशित: (2020) -
The Effects of Data Imputation on Covariance and Inverse Covariance Matrix Estimation
द्वारा: Tuan L. Vo, और अन्य
प्रकाशित: (2024-01-01) -
<i>k</i>-Covariance: An Approach of Ensemble Covariance Estimation and Undersampling to Stabilize the Covariance Matrix in the Global Minimum Variance Portfolio
द्वारा: Tuan Tran, और अन्य
प्रकाशित: (2022-06-01)