Predictive performance of Dirichlet process shrinkage methods in linear regression

DOI10.1016/J.CSDA.2007.12.005zbMath1452.62506OpenAlexW2032985891MaRDI QIDQ1023703

David J. Nott

Publication date: 12 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2007.12.005

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Bayesian inference (62F15)

Related Items (2)

Sparse Bayesian hierarchical modeling of high-dimensional clustering problems ⋮ A Bayesian approach to multicollinearity and the simultaneous selection and clustering of predictors in linear regression

Uses Software

• SemiPar

Cites Work

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• On a class of Bayesian nonparametric estimates: I. Density estimates
• Nonparametric regression using Bayesian variable selection
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Heuristics of instability and stabilization in model selection
• Ferguson distributions via Polya urn schemes
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• A Bayesian analysis of some nonparametric problems
• Averaged gene expressions for regression
• A semiparametric Bayesian model for randomised block designs
• Estimating Normal Means with a Dirichlet Process Prior
• Bayesian Model Averaging for Linear Regression Models
• A Bayesian Population Model With Hierarchical Mixture Priors Applied to Blood Count Data
• Semiparametric Regression
• A Semiparametric Bayesian Approach to the Random Effects Model
• Sparsity and Smoothness Via the Fused Lasso
• Gibbs Sampling Methods for Stick-Breaking Priors
• Estimating normal means with a conjugate style dirichlet process prior
• Bayesian Density Estimation and Inference Using Mixtures
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems

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