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James-Stein confidence set: Equal area approach to the global approximation of coverage probability of squares of the means of p ≥ 3 normal distributions subject to confidence estimation. As numerical examples
Confidence sets based on the positive part James–Stein estimator with the asymptotically constant coverage probability depends on the non-centrality parameter τ2 (the sum of the squares of the means of normal
James-Stein confidence set: Equal area approach to the global approximation of coverage probability of squares of the means of p ≥ 3 normal distributions subject to confidence estimation. As numerical examples
Confidence sets based on the positive part James–Stein estimator with the asymptotically constant coverage probability depends on the non-centrality parameter τ2 (the sum of the squares of the means of normal
Asymptotic expansion of the coverage probability of James-stein estimatorsMultivariate normal distribution
Asymptotic expansion of the coverage probability of James-stein estimatorsMultivariate normal distribution
Hyperparameters of Multilayer Perceptron with Normal Distributed Weights on multivariate normality test
and proves that the neuron vectors are considerably far from Gaussian distribution.
Total probability formula for vector gaussian distributions with the multivariate probability distributions. The necessary integrals are not available in the literature. One
Limits of Risks Ratios of Shrinkage Estimators under the Balanced Loss FunctionIn this paper we study the estimation of a multivariate normal mean under the balanced loss
Baranchick-type Estimators of a Multivariate Normal Mean Under the General Quadratic Loss FunctionThe problem of estimating the mean of a multivariate normal distribution by different types
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