Adaptive Bayesian density regression for high-dimensional data

DOI10.3150/14-BEJ663zbMath1388.62115arXiv1403.2695OpenAlexW3098387724WikidataQ57433433 ScholiaQ57433433MaRDI QIDQ5963506

Weining Shen, Subhashis Ghosal

Publication date: 22 February 2016

Published in: Bernoulli (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1403.2695

zbMATH Keywords

adaptive estimationvariable selectiondensity regressionhigh-dimensional modelsMCMC-free computationnonparametric Bayesian inferenceposterior contraction rate

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Bayesian inference (62F15)

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Cites Work

• Unnamed Item
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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Multivariate density estimation under sup-norm loss: oracle approach, adaptation and independence structure
• Posterior consistency in conditional distribution estimation
• Mirror averaging with sparsity priors
• Statistics for high-dimensional data. Methods, theory and applications.
• Rates of contraction of posterior distributions based on Gaussian process priors
• Variable selection in nonparametric additive models
• Posterior convergence rates of Dirichlet mixtures at smooth densities
• Convergence rates of posterior distributions for non iid observations
• Persistene in high-dimensional linear predictor-selection and the virtue of overparametrization
• Convergence rates of posterior distributions.
• Rates of convergence of posterior distributions.
• Needles and straw in a haystack: posterior concentration for possibly sparse sequences
• Adaptive estimation of multivariate functions using conditionally Gaussian tensor-product spline priors
• On estimating a density using Hellinger distance and some other strange facts
• High-dimensional generalized linear models and the lasso
• Bayesian variable selection for high dimensional generalized linear models: convergence rates of the fitted densities
• Posterior concentration rates for infinite dimensional exponential families
• Bayesian density regression with logistic Gaussian process and subspace projection
• Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
• A crossvalidation method for estimating conditional densities
• The multivariate beta process and an extension of the Polya tree model
• A class of mixtures of dependent tail-free processes
• POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES
• Adaptive Bayesian Procedures Using Random Series Priors
• Kernel stick-breaking processes
• Bayesian curve fitting using multivariate normal mixtures
• Estimation of conditional densities and sensitivity measures in nonlinear dynamical systems
• Multivariate Bayesian Variable Selection and Prediction
• Methods for Estimating a Conditional Distribution Function
• Bayes Model Averaging with Selection of Regressors
• Adaptive Bayesian multivariate density estimation with Dirichlet mixtures
• Bayesian Density Regression
• Order-Based Dependent Dirichlet Processes
• A practical guide to splines.

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