Pages that link to "Item:Q2350068"
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The following pages link to A unified approach to estimating a normal mean matrix in high and low dimensions (Q2350068):
Displaying 16 items.
- Estimation of a high-dimensional covariance matrix with the Stein loss (Q276961) (← links)
- Estimation of (near) low-rank matrices with noise and high-dimensional scaling (Q548547) (← links)
- Estimating a mean matrix: boosting efficiency by multiple affine shrinkage (Q734400) (← links)
- Unified improvements in estimation of a normal covariance matrix in high and low dimensions (Q900805) (← links)
- Improved loss estimation for a normal mean matrix (Q1755127) (← links)
- Weighted shrinkage estimators of normal mean matrices and dominance properties (Q2111070) (← links)
- Ridge-type linear shrinkage estimation of the mean matrix of a high-dimensional normal distribution (Q2181723) (← links)
- Covariance matrix estimation under data-based loss (Q2244574) (← links)
- Bayes minimax competitors of preliminary test estimators in \(k\) sample problems (Q2329832) (← links)
- James-Stein estimation problem for a multivariate normal random matrix and an improved estimator (Q2401292) (← links)
- Estimating the mean and variance of a high-dimensional normal distribution using a mixture prior (Q2419159) (← links)
- Generalized Bayes estimators with closed forms for the normal mean and covariance matrices (Q2676905) (← links)
- High dimensional matrix estimation with unknown variance of the noise (Q2960507) (← links)
- (Q5011447) (← links)
- Truncated Estimators for a Precision Matrix (Q6497053) (← links)
- An adaptive singular value shrinkage for estimation problem of low-rank matrix mean with unknown covariance matrix (Q6578504) (← links)
