Name
Title | ||
|---|---|---|
Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions. |
Abstract | ||
|---|---|---|
This paper considers the problem of estimating a high-dimensional covariance matrix in a low sample support situation where the sample size is smaller, or not much larger, than the dimensionality of the data, which could potentially be very large. We develop a regularized sample covariance matrix (RSCM) estimator which can be applied in commonly occurring high-dimensional data problems. The propos... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TSP.2019.2908144 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Covariance matrices,Symmetric matrices,Estimation,Eigenvalues and eigenfunctions,Portfolios,Benchmark testing,Indexes | Applied mathematics,Mathematical optimization,Symmetric probability distribution,Minimum mean square error,Symmetric matrix,Curse of dimensionality,Portfolio optimization,Covariance matrix,Sample size determination,Mathematics,Estimator | Journal |
Volume | Issue | ISSN |
67 | 10 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esa Ollila | 1 | 351 | 33.51 |
| Elias Raninen | 2 | 0 | 1.35 |