An application of a minimax Bayes rule and shrinkage estimators to the portfolio selection problem under the Bayesian approach

DOI10.1007/BF02763003zbMath1102.62006MaRDI QIDQ855247

Hiroyuki Kashima

Publication date: 4 January 2007

Published in: Statistical Papers (Search for Journal in Brave)

zbMATH Keywords

tablesgraphicsmaximization of expected utilityminimax Bayes rule

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian problems; characterization of Bayes procedures (62C10) Minimax procedures in statistical decision theory (62C20)

Related Items (2)

Estimation of the optimal portfolio weights by shrinking the mean vector towards a linear subspace ⋮ Dominance of a class of Stein type estimators for optimal portfolio weights when the covariance matrix is unknown

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Statistical decision theory and Bayesian analysis. 2nd ed
• Estimation of the mean of a multivariate normal distribution
• Admissible minimax estimation of a multivariate normal mean with arbitrary quadratic loss
• A generalized binomial distribution determined by a two-state Markov chain and a distribution by the Bayesian approach
• A unified and broadened class of admissible minimax estimators of a multivariate normal mean
• Improving on the James-Stein positive-part estimator
• Controlled shrinkage estimators (a class of estimators better than the least squares estimator, with respect to a general quadratic loss, for normal observations

This page was built for publication: An application of a minimax Bayes rule and shrinkage estimators to the portfolio selection problem under the Bayesian approach