Pages that link to "Item:Q2482607"
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The following pages link to Bayes minimax estimators of the mean of a scale mixture of multivariate normal distributions (Q2482607):
Displaying 16 items.
- Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise (Q143154) (← links)
- Generalized Bayes minimax estimators of location vectors for spherically symmetric distributions with residual vector (Q464376) (← links)
- Bayes minimax estimation of the multivariate normal mean vector for the case of common unknown variance (Q549918) (← links)
- Minimax estimation of means of multivariate normal mixtures (Q918603) (← links)
- Generalized Bayes minimax estimators of the mean of multivariate normal distribution with unknown variance (Q957306) (← links)
- ``Almost arbitrary'' minimax estimators of location for independent component variance mixtures of normal variates (Q1115049) (← links)
- On the construction of Bayes minimax estimators (Q1807097) (← links)
- Admissible minimax estimators of a mean vector of scale mixtures of multivariate normal distributions (Q1873110) (← links)
- Bayes minimax estimators of a location vector for densities in the Berger class (Q1950840) (← links)
- Predictive density estimation under the Wasserstein loss (Q2189119) (← links)
- Generalized Bayes minimax estimators of location vectors for spherically symmetric distribu\-tions (Q2482626) (← links)
- A new class of minimax generalized Bayes estimators of a normal variance (Q2500642) (← links)
- Stein estimation for spherically symmetric distributions: recent developments (Q2634651) (← links)
- A review of Brown 1971 (in)admissibility results under scale mixtures of Gaussian priors (Q2676892) (← links)
- Bayesian estimation of multivariate normal mixtures with covariate-dependent mixing weights, with an application in antimicrobial resistance monitoring (Q3134220) (← links)
- Bayes minimax estimators of a multivariate normal mean, with application to generalized ridge regression (Q3316397) (← links)