Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes

DOI10.1080/01621459.2013.838568zbMath1283.62091arXiv1201.4403OpenAlexW2047051695WikidataQ34334668 ScholiaQ34334668MaRDI QIDQ5406371

Bin Zhu, David B. Dunson

Publication date: 1 April 2014

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1201.4403

zbMATH Keywords

stochastic differential equationsreproducing kernel Hilbert spacenested smoothing splinespenalized sum-of-squares

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Bayesian inference (62F15) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Numerical analysis or methods applied to Markov chains (65C40) Applications of functional analysis in probability theory and statistics (46N30)

Related Items

Predictions based on the clustering of heterogeneous functions via shape and subject-specific covariates, Bayesian dynamic financial networks with time-varying predictors, PiPs: a kernel-based optimization scheme for analyzing non-stationary 1D signals, Locally adaptive smoothing with Markov random fields and shrinkage priors, Sharp bounds on distribution functions and expectations of mixtures of ordered families of distributions

Uses Software

• R
• bsml

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Variable selection for nonparametric Gaussian process priors: Models and computational strategies
• Bayesian forecasting and dynamic models.
• Posterior consistency of Gaussian process prior for nonparametric binary regression
• Rates of contraction of posterior distributions based on Gaussian process priors
• Locally adaptive Bayesian P-splines with a normal-exponential-gamma prior
• Nonparametric regression using Bayesian variable selection
• Multivariate adaptive regression splines
• Weak convergence and empirical processes. With applications to statistics
• Improved inference in nonparametric regression using \(L_ k\)-smoothing splines
• On posterior consistency in nonparametric regression problems
• Bayesian curve-fitting with free-knot splines
• Stochastic Functional Data Analysis: A Diffusion Model-Based Approach
• Gaussian Process Regression Analysis for Functional Data
• Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models
• Automatic Bayesian Curve Fitting
• Ideal spatial adaptation by wavelet shrinkage
• Bayesian mixture of splines for spatially adaptive nonparametric regression
• A simple and efficient simulation smoother for state space time series analysis
• Penalized regression with model-based penalties
• Spatially Adaptive Regression Splines and Accurate Knot Selection Schemes
• Spatially adaptive smoothing splines
• Radon-Nikodym Derivatives of Gaussian Measures