Locally adaptive smoothing with Markov random fields and shrinkage priors

DOI10.1214/17-BA1050WikidataQ54996555 ScholiaQ54996555MaRDI QIDQ1752016

James R. Faulkner, Vladimir N. Minin

Publication date: 25 May 2018

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1512.06505

zbMATH Keywords

Lévy processHamiltonian Monte Carlohorseshoe priorlocally adaptive nonparametric curve fitting method

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Markov processes: hypothesis testing (62M02)

Related Items (9)

A Bayesian model of dose-response for cancer drug studies ⋮ Horseshoe shrinkage methods for Bayesian fusion estimation ⋮ Fast and locally adaptive Bayesian quantile smoothing using calibrated variational approximations ⋮ Baseline drift estimation for air quality data using quantile trend filtering ⋮ Bayesian Change Point Detection with Spike-and-Slab Priors ⋮ Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo ⋮ Locally adaptive Bayesian isotonic regression using half shrinkage priors ⋮ Locally adaptive smoothing with Markov random fields and shrinkage priors ⋮ Bayesian analysis of spatial generalized linear mixed models with Laplace moving average random fields

Uses Software

• R
• GMRFLib
• Stan
• EBayesThresh
• RStan
• NUTS

Cites Work

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• Fast Sampling of Gaussian Markov Random Fields
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
• Priors for Bayesian adaptive spline smoothing
• A general framework for the parametrization of hierarchical models
• The solution path of the generalized lasso
• Bayesian adaptive smoothing splines using stochastic differential equations
• Some priors for sparse regression modelling
• Gaussian process for nonstationary time series prediction
• Locally adaptive Bayesian P-splines with a normal-exponential-gamma prior
• Asymptotic inference for a change-point Poisson process
• Langevin diffusions and Metropolis-Hastings algorithms
• Function estimation with locally adaptive dynamic models
• The horseshoe+ estimator of ultra-sparse signals
• Inference from iterative simulation using multiple sequences
• Locally adaptive smoothing with Markov random fields and shrinkage priors
• Penalising model component complexity: a principled, practical approach to constructing priors
• Adaptive piecewise polynomial estimation via trend filtering
• Smoothing by spline functions.
• Empirical Bayes selection of wavelet thresholds
• On the half-Cauchy prior for a global scale parameter
• Inference with normal-gamma prior distributions in regression problems
• Penalized regression, standard errors, and Bayesian Lassos
• Bayesian curve-fitting with free-knot splines
• Bayesian inference for generalized linear mixed models
• On the Second‐Order Random Walk Model for Irregular Locations
• The horseshoe estimator for sparse signals
• $\ell_1$ Trend Filtering
• The Bayesian Lasso
• Non-Gaussian State-Space Modeling of Nonstationary Time Series
• On scale mixtures of normal distributions
• Bayesian Variable Selection in Linear Regression
• A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices
• Wavelet Thresholding via A Bayesian Approach
• Fully Bayesian spline smoothing and intrinsic autoregressive priors
• On Block Updating in Markov Random Field Models for Disease Mapping
• Regression Shrinkage and Selection via The Lasso: A Retrospective
• Gaussian Markov Random Fields
• A Statistical View of Some Chemometrics Regression Tools
• Hierarchical Bayesian Analysis of Changepoint Problems
• Generalized double Pareto shrinkage
• Wavelet Shrinkage with Double Weibull Prior
• Dirichlet–Laplace Priors for Optimal Shrinkage
• Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Local Shrinkage Rules, Lévy Processes and Regularized Regression
• THE TIME INTERVALS BETWEEN INDUSTRIAL ACCIDENTS
• Smoothing noisy data with spline functions
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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