Improved prediction for a multivariate normal distribution with unknown mean and variance

DOI10.1007/s10463-007-0163-zzbMath1332.62030OpenAlexW1976908126MaRDI QIDQ841018

Kengo Kato

Publication date: 14 September 2009

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-007-0163-z

zbMATH Keywords

Kullback-Leibler divergencemultivariate normal distributionBayesian predictionmultivariate \(t\)-distributionshrinkage priorstar orderingright invariant prior

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20)

Related Items (13)

From minimax shrinkage estimation to minimax shrinkage prediction ⋮ Minimaxity in predictive density estimation with parametric constraints ⋮ Predictive density estimation under the Wasserstein loss ⋮ Optimal shrinkage estimation of predictive densities under \(\alpha\)-divergences ⋮ Predictive density estimators with integrated \(L_1\) loss ⋮ Density prediction and the Stein phenomenon ⋮ On predictive density estimation with additional information ⋮ Unnamed Item ⋮ On predictive density estimation for gamma models with parametric constraints ⋮ On efficient prediction and predictive density estimation for normal and spherically symmetric models ⋮ Minimax predictive density for sparse count data ⋮ On discrete priors and sparse minimax optimal predictive densities ⋮ Bayesian predictive density estimation for a chi-squared model using information from a normal observation with unknown mean and variance

Cites Work

• Unnamed Item
• Dispersive ordering-some applications and examples
• Admissible predictive density estimation
• Improved minimax predictive densities under Kullback-Leibler loss
• Generalized Bayes minimax estimators of the multivariate normal mean with unknown covariance matrix
• A shrinkage predictive distribution for multivariate Normal observables
• Exact Minimax Strategies for Predictive Density Estimation, Data Compression, and Model Selection
• On asymptotic properties of predictive distributions
• Goodness of prediction fit
• Dispersive ordering of distributions

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