Bayesian shrinkage prediction for the regression problem

DOI10.1016/j.jmva.2008.01.014zbMath1169.62019arXivmath/0701583OpenAlexW2039318965MaRDI QIDQ953850

Fumiyasu Komaki, Kei Kobayashi

Publication date: 6 November 2008

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/math/0701583

zbMATH Keywords

superharmonic functionsshrinkage estimationminimaxityKullback-Leibler divergenceBayesian predictionnormal regression

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Bayesian inference (62F15) Bayesian problems; characterization of Bayes procedures (62C10)

Related Items (7)

On minimax optimality of sparse Bayes predictive density estimates ⋮ Asymptotic properties of Bayesian predictive densities when the distributions of data and target variables are different ⋮ From minimax shrinkage estimation to minimax shrinkage prediction ⋮ Inadmissibility of the corrected Akaike information criterion ⋮ Unnamed Item ⋮ Minimax predictive density for sparse count data ⋮ Simultaneous prediction for independent Poisson processes with different durations

Uses Software

• ElemStatLearn

Cites Work

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• A shrinkage predictive distribution for multivariate Normal observables
• Exact Minimax Strategies for Predictive Density Estimation, Data Compression, and Model Selection
• PREDICTIVE DENSITY ESTIMATION FOR MULTIPLE REGRESSION
• On asymptotic properties of predictive distributions
• On the estimation of parametric density functions
• A Note on the Estimation of Probability Density Functions
• Applications of Differential Geometry to Econometrics
• The elements of statistical learning. Data mining, inference, and prediction

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