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Uncertainty quantification for Bayesian CART (Q2073718)

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Language	Label	Description	Also known as
English	Uncertainty quantification for Bayesian CART	scientific article

Statements

scholarly article

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Uncertainty quantification for Bayesian CART (English)

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Ismaël Castillo

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Veronika Rockova

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The Annals of Statistics

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publication date

7 February 2022

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full work available at URL

https://arxiv.org/abs/1910.07635

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zbMATH Keywords

Bayesian CART

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nonparametric Bernstein-von Mises theorem

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posterior concentration

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recursive partitioning

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regression trees

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describes a project that uses

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MaRDI profile type

MaRDI publication profile

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Bayesian Optimal Adaptive Estimation Using a Sieve Prior

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Model-Based Compressive Sensing

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Optimal predictive model selection.

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Criteria for Bayesian model choice with application to variable selection

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Learning Theory

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Variable selection for BART: an application to gene regulation

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Asymptotic equivalence of nonparametric regression and white noise

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Honest adaptive confidence bands and self-similar functions

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On information pooling, adaptability and superefficiency in nonparametric function estimation

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Lower bounds for posterior rates with Gaussian process priors

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On Bayesian supremum norm contraction rates

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Pólya tree posterior distributions on densities

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Spike and slab Pólya tree posterior densities: adaptive inference

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Nonparametric Bernstein-von Mises theorems in Gaussian white noise

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On the Bernstein-von Mises phenomenon for nonparametric Bayes procedures

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BART: Bayesian additive regression trees

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Adaptive Bayesian Wavelet Shrinkage

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Wavelets on the interval and fast wavelet transforms

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A Bayesian CART algorithm

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CART and best-ortho-basis: a connection

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A simple wavelet approach to nonparametric regression from recursive partitioning schemes

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Benchmark priors for Bayesian model averaging.

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The risk inflation criterion for multiple regression

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Unbalanced Haar Technique for Nonparametric Function Estimation

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Model Selection for CART Regression Trees

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Convergence rates of posterior distributions.

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Fundamentals of Nonparametric Bayesian Inference

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Rates of contraction for posterior distributions in \(L^{r}\)-metrics, \(1 \leq r \leq \infty\)

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Mathematical Foundations of Infinite-Dimensional Statistical Models

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A new class of unbalanced Haar wavelets that form an unconditional basis for \(L_ p\) on general measure spaces

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Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion)

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On adaptive posterior concentration rates

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A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion

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Bayesian Regression Trees for High-Dimensional Prediction and Variable Selection

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Bayesian Regression Tree Ensembles that Adapt to Smoothness and Sparsity

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Nonparametric statistical inference for drift vector fields of multi-dimensional diffusions

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Bernstein-von Mises theorems for statistical inverse problems. II: Compound Poisson processes

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Adaptive Bernstein-von Mises theorems in Gaussian white noise

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Posterior concentration for Bayesian regression trees and forests

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Consistency of random forests

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Minimax-optimal classification with dyadic decision trees

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Adaptive Bayesian density estimation in \(L^p\)-metrics with Pitman-Yor or normalized inverse-Gaussian process kernel mixtures

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Frequentist coverage of adaptive nonparametric Bayesian credible sets

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

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Multiscale Poisson Intensity and Density Estimation

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Optional Pólya tree and Bayesian inference

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Supremum norm posterior contraction and credible sets for nonparametric multivariate regression

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Identifiers

zbMATH Open document ID

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10.1214/21-AOS2093

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Mathematics Subject Classification ID

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zbMATH DE Number

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Sitelinks

Mathematics(1 entry)

mardi Publication:2073718

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