From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction

Edward I. George, Feng Liang, Xinyi Xu

Research output: Contribution to journalArticlepeer-review


In a remarkable series of papers beginning in 1956, Charles Stein set the stage for the future development of minimax shrinkage estimators of a multivariate normal mean under quadratic loss. More recently, parallel developments have seen the emergence of minimax shrinkage estimators of multivariate normal predictive densities under Kullback-Leibler risk. We here describe these parallels emphasizing the focus on Bayes procedures and the derivation of the superharmonic conditions for minimaxity as well as further developments of new minimax shrinkage predictive density estimators including multiple shrinkage estimators, empirical Bayes estimators, normal linear model regression estimators and nonparametric regression estimators.

Original languageEnglish (US)
Pages (from-to)82-94
Number of pages13
JournalStatistical Science
Issue number1
StatePublished - Feb 2012


  • Asymptotic minimaxity
  • Bayesian prediction
  • Empirical bayes
  • Inadmissibility
  • Marginals
  • Multiple shrinkage
  • Prior distributions
  • Superharmonic
  • Unbiased estimates of risk

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty


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