Bayesian regression with nonparametric heteroskedasticity

DOI10.1016/j.jeconom.2014.12.006zbMath1331.62238OpenAlexW2084631652MaRDI QIDQ2343818

Andriy Norets

Publication date: 6 May 2015

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2014.12.006

zbMATH Keywords

multivariate Bernstein polynomialsmisspecificationsemiparametric efficiencyheteroskedasticityposterior consistencyBayesian linear regressionGaussian process priorssemiparametric Bernstein-von Mises theorem

Mathematics Subject Classification ID

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

Related Items (4)

A Dirichlet process functional approach to heteroscedastic-consistent covariance estimation ⋮ Classical \(p\)-values and the Bayesian posterior probability that the hypothesis is approximately true ⋮ Frequentist properties of Bayesian inequality tests ⋮ Bayesian inference for partially identified smooth convex models

Uses Software

• MathOverflow

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Finite sample Bernstein-von Mises theorem for semiparametric problems
• Smoothly mixing regressions
• Nonparametric Bernstein-von Mises theorems in Gaussian white noise
• Posterior consistency in conditional distribution estimation
• Rates of contraction for posterior distributions in \(L^{r}\)-metrics, \(1 \leq r \leq \infty\)
• The semiparametric Bernstein-von Mises theorem
• On the Bernstein-von Mises phenomenon for nonparametric Bayes procedures
• A semiparametric Bernstein-von Mises theorem for Gaussian process priors
• Bernstein-von Mises theorem for linear functionals of the density
• On conditional variance estimation in nonparametric regression
• Adaptive Bayesian estimation using a Gaussian random field with inverse gamma bandwidth
• Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density
• Rates of contraction of posterior distributions based on Gaussian process priors
• Approximation of conditional densities by smooth mixtures of regressions
• Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information
• Adapting for heteroscedasticity in linear models
• An MCMC approach to classical estimation.
• On Bayesian adaptation
• Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution
• New approaches to Bayesian consistency
• Posterior consistency of Dirichlet mixtures in density estimation
• Asymptotic efficiency in estimation with conditional moment restrictions
• Convergence rates of posterior distributions.
• Rates of convergence of posterior distributions.
• Convergence rates for density estimation with Bernstein polynomials.
• Bayesian nonparametrics
• Convergence rates for posterior distributions and adaptive estimation
• Weak convergence and empirical processes. With applications to statistics
• The Bernstein-von Mises theorem under misspecification
• Adaptive Bayesian density estimation with location-scale mixtures
• The consistency of posterior distributions in nonparametric problems
• Convergence rates for Bayesian density estimation of infinite-dimensional exponential families
• Nonparametric Bayesian model selection and averaging
• Posterior convergence rates for Dirichlet mixtures of beta densities
• Posterior consistency of logistic Gaussian process priors in density estimation
• Quasi-Bayesian analysis of nonparametric instrumental variables models
• Copula based factorization in Bayesian multivariate infinite mixture models
• Misspecification in infinite-dimensional Bayesian statistics
• Bayesian regression with heteroscedastic error density and parametric mean function
• Regression density estimation using smooth adaptive Gaussian mixtures
• Bayesian density regression with logistic Gaussian process and subspace projection
• Risk of Bayesian Inference in Misspecified Models, and the Sandwich Covariance Matrix
• Bayesian Interpretations of Heteroskedastic Consistent Covariance Estimators Using the Informed Bayesian Bootstrap
• Nonparametric Bayes Conditional Distribution Modeling With Variable Selection
• POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES
• Information Theory and Statistical Mechanics
• Kernel stick-breaking processes
• Pseudo Maximum Likelihood Methods: Theory
• Asymptotically Efficient Estimation in the Presence of Heteroskedasticity of Unknown Form
• Asymptotic Statistics
• Bayesian density estimation using bernstein polynomials
• Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition
• Consistency of Bernstein Polynomial Posteriors
• Bayesian mixture of splines for spatially adaptive nonparametric regression
• Asymptotic Normality of Semiparametric and Nonparametric Posterior Distributions
• Probability for Statisticians
• On the Optimum Rate of Transmitting Information
• An ANOVA Model for Dependent Random Measures
• On Bayes procedures
• Contemporary Bayesian Econometrics and Statistics
• Order-Based Dependent Dirichlet Processes
• Convergence of stochastic processes
• Maximum Likelihood Estimation of Misspecified Models
• Handbook of econometrics. Vol. 4

This page was built for publication: Bayesian regression with nonparametric heteroskedasticity