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Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach

https://doi.org/10.1198/jasa.2011.tm10518

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4612-0567-8_19openalex-snapshot

Empirical Bayes Estimation in Wavelet Nonparametric Regression

inferredcites →https://doi.org/10.1007/978-1-4612-2414-3_13openalex-snapshot

Orthogonalizations and Prior Distributions for Orthogonalized Model Mixing

inferredcites →https://doi.org/10.1007/978-3-642-59657-5_13openalex-snapshot

Methods for Generating Random Orthogonal Matrices

inferredcites →https://doi.org/10.1007/bf01456804openalex-snapshot

Das asymptotische Verteilungsgesetz der Eigenwerte linearer partieller Differentialgleichungen (mit einer Anwendung auf die Theorie der Hohlraumstrahlung)

inferredcites →https://doi.org/10.1007/bf02888369openalex-snapshot

Posterior odds ratios for selected regression hypotheses

inferredcites →https://doi.org/10.1016/0304-4076(81)90082-8openalex-snapshot

Posterior odds ratios for regression hypotheses

inferredcites →https://doi.org/10.1016/s0304-4076(00)00076-2openalex-snapshot

Benchmark priors for Bayesian model averaging

inferredcites →https://doi.org/10.1017/cbo9780511812651openalex-snapshot

Pattern Recognition and Neural Networks

inferredcites →https://doi.org/10.1038/109132a0openalex-snapshot

The Theory of Probability

inferredcites →https://doi.org/10.1080/00401706.1989.10488470openalex-snapshot

Flexible Parsimonious Smoothing and Additive Modeling

inferredcites →https://doi.org/10.1080/01621459.1987.10478458openalex-snapshot

The Calculation of Posterior Distributions by Data Augmentation

inferredcites →https://doi.org/10.1080/01621459.1990.10476213openalex-snapshot

Sampling-Based Approaches to Calculating Marginal Densities

inferredcites →https://doi.org/10.1080/01621459.1993.10476321openalex-snapshot

Bayesian Analysis of Binary and Polychotomous Response Data

inferredcites →https://doi.org/10.1080/01621459.1994.10476829openalex-snapshot

The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem

inferredcites →https://doi.org/10.1080/01621459.1996.10476989openalex-snapshot

Prediction Via Orthogonalized Model Mixing

inferredcites →https://doi.org/10.1080/01621459.1997.10473662openalex-snapshot

Adaptive Bayesian Wavelet Shrinkage

inferredcites →https://doi.org/10.1080/01621459.1997.10474045openalex-snapshot

Computing Bayes Factors by Combining Simulation and Asymptotic Approximations

inferredcites →https://doi.org/10.1080/01621459.1999.10473879openalex-snapshot

Parameter Expansion for Data Augmentation

inferredcites →https://doi.org/10.1080/10618600.1995.10474672openalex-snapshot

Conditioning in Markov Chain Monte Carlo

inferredcites →https://doi.org/10.1090/s0273-0979-00-00865-xopenalex-snapshot

Eigenvalues, invariant factors, highest weights, and Schubert calculus

inferredcites →https://doi.org/10.1093/acprof:oso/9780199694587.003.0017openalex-snapshot

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction*

inferredcites →https://doi.org/10.1093/biomet/81.1.27openalex-snapshot

Covariance Structure of the Gibbs Sampler with Applications to the Comparisons of Estimators and Augmentation Schemes

inferredcites →https://doi.org/10.1093/biomet/85.2.391openalex-snapshot

Multiple shrinkage and subset selection in wavelets

inferredcites →https://doi.org/10.1093/biomet/86.2.301openalex-snapshot

Seeking efficient data augmentation schemes via conditional and marginal augmentation

inferredcites →https://doi.org/10.1093/biomet/92.4.747openalex-snapshot

Adaptive sampling for Bayesian variable selection

inferredcites →https://doi.org/10.1093/biomet/asq017openalex-snapshot

The horseshoe estimator for sparse signals

inferredcites →https://doi.org/10.1111/1467-9868.00128openalex-snapshot

Automatic Bayesian Curve Fitting

inferredcites →https://doi.org/10.1111/1467-9868.00257openalex-snapshot

Flexible Empirical Bayes Estimation for Wavelets

inferredcites →https://doi.org/10.1111/j.1467-9574.2005.00275.xopenalex-snapshot

Posterior model probabilities via path‐based pairwise priors

inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot

Maximum Likelihood from Incomplete Data Via the EM Algorithm

inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02015.xopenalex-snapshot

Assessment and Propagation of Model Uncertainty

inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot

Regression Shrinkage and Selection Via the Lasso

inferredcites →https://doi.org/10.1198/016214507000001337openalex-snapshot

Mixtures of g Priors for Bayesian Variable Selection

inferredcites →https://doi.org/10.1198/1061860042958openalex-snapshot

Bayesian Variable Selection and the Swendsen-Wang Algorithm

inferredcites →https://doi.org/10.1198/106186008x382683openalex-snapshot

Feature-Inclusion Stochastic Search for Gaussian Graphical Models

inferredcites →https://doi.org/10.1198/jcgs.2010.09049openalex-snapshot

Bayesian Adaptive Sampling for Variable Selection and Model Averaging

inferredcites →https://doi.org/10.1214/009053604000000030openalex-snapshot

Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences

inferredcites →https://doi.org/10.1214/009053604000000067openalex-snapshot

Least angle regression

inferredcites →https://doi.org/10.1214/009053604000001147openalex-snapshot

Spike and slab variable selection: Frequentist and Bayesian strategies

inferredcites →https://doi.org/10.1214/009053605000000345openalex-snapshot

Empirical Bayes selection of wavelet thresholds

inferredcites →https://doi.org/10.1214/009053607000000569openalex-snapshot

A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms

inferredcites →https://doi.org/10.1214/06-ba105openalex-snapshot

Bayesian auxiliary variable models for binary and multinomial regression

inferredcites →https://doi.org/10.1214/088342304000000035openalex-snapshot

Model Uncertainty

inferredcites →https://doi.org/10.1214/ss/1009212519openalex-snapshot

Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors

inferredcites →https://doi.org/10.2307/2288585openalex-snapshot

Multivariate Statistics: A Vector Space Approach.

inferredcites →https://doi.org/10.2307/2289776openalex-snapshot

Sampling-Based Approaches to Calculating Marginal Densities

inferredcites →https://doi.org/10.2307/2291091openalex-snapshot

Bayes Factors

inferredcites →https://doi.org/10.2307/2291462openalex-snapshot

Bayesian Model Averaging for Linear Regression Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.2139/ssrn.3971832openalex-extracted

Bayesian Approaches to Shrinkage and Sparse Estimation

inferred← citeshttps://doi.org/10.1002/9781118445112.stat08324openalex-snapshot

Computational Statistics and Data Science in the Twenty‐First Century

inferred← citeshttps://doi.org/10.1002/wics.1352openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-06784-6_13openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/978-3-031-66961-3_26openalex-snapshot

Technology and Information: Data Augmentation Applied to Industry in Modern Times

inferred← citeshttps://doi.org/10.1007/s11222-011-9312-6openalex-snapshot

Secure Bayesian model averaging for horizontally partitioned data

inferred← citeshttps://doi.org/10.1007/s11222-024-10509-2openalex-snapshot

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inferred← citeshttps://doi.org/10.1007/s13571-025-00391-xopenalex-snapshot

An Online Algorithm for Bayesian Variable Selection in Logistic Regression Models With Streaming Data

inferred← citeshttps://doi.org/10.1016/j.csda.2014.03.004openalex-snapshot

On the use of marginal posteriors in marginal likelihood estimation via importance sampling

inferred← citeshttps://doi.org/10.1016/j.csda.2014.07.014openalex-snapshot

Sandwich algorithms for Bayesian variable selection

inferred← citeshttps://doi.org/10.1016/j.spasta.2019.01.003openalex-snapshot

Efficient Bayesian modeling of large lattice data using spectral properties of Laplacian matrix

inferred← citeshttps://doi.org/10.1016/j.spasta.2026.100988openalex-snapshot

Computationally efficient approximations to conditional autoregressive prior for spatial and spatiotemporal datasets over large lattices

inferred← citeshttps://doi.org/10.1038/s41467-024-48201-8openalex-snapshot

Temporal shifts in 24 notifiable infectious diseases in China before and during the COVID-19 pandemic

inferred← citeshttps://doi.org/10.1080/00031305.2015.1031827openalex-snapshot

Bayesian Variable Selection Under Collinearity

inferred← citeshttps://doi.org/10.1080/01621459.2014.980908openalex-snapshot

Scalable Bayesian Model Averaging Through Local Information Propagation

inferred← citeshttps://doi.org/10.1080/01621459.2017.1360778openalex-snapshot

Particle EM for Variable Selection

inferred← citeshttps://doi.org/10.1080/01621459.2025.2537461openalex-snapshot

Provably Efficient Posterior Sampling for Sparse Linear Regression via Measure Decomposition

inferred← citeshttps://doi.org/10.1080/02664763.2025.2511938openalex-snapshot

Robust Bayesian model averaging for linear regression models with heavy-tailed errors

inferred← citeshttps://doi.org/10.1080/10618600.2025.2473932openalex-snapshot

Gibbs Sampling Using Anti-Correlation Gaussian Data Augmentation, with Applications to L1-Ball-Type Models

inferred← citeshttps://doi.org/10.1093/biomet/asaa055openalex-snapshot

In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p

inferred← citeshttps://doi.org/10.1101/148965openalex-snapshot

Parallel Computing to Speed up Whole-Genome Bayesian Regression Analyses Using Orthogonal Data Augmentation

inferred← citeshttps://doi.org/10.1111/biom.13823openalex-snapshot

Model Uncertainty Quantification in Cox Regression

inferred← citeshttps://doi.org/10.1111/joes.12094openalex-snapshot

WEIGHTED‐AVERAGE LEAST SQUARES (WALS): A SURVEY

inferred← citeshttps://doi.org/10.1111/rssb.12185openalex-snapshot

Control Functionals for Monte Carlo Integration

inferred← citeshttps://doi.org/10.1142/s0219455426501543openalex-snapshot

Diagnosis Method for Abnormal Data in Bridge Health Monitoring Using BMA-Assisted EasyEnsemble-AlexNet

inferred← citeshttps://doi.org/10.1186/s12711-020-00533-xopenalex-snapshot

Fast parallelized sampling of Bayesian regression models for whole-genome prediction

inferred← citeshttps://doi.org/10.1214/14-aoas788openalex-snapshot

Inferring causal impact using Bayesian structural time-series models

inferred← citeshttps://doi.org/10.1214/17-ba1051openalex-snapshot

On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression

inferred← citeshttps://doi.org/10.14710/medstat.15.1.83-93openalex-snapshot

FORECASTING COVID-19 IN INDONESIA WITH VARIOUS TIME SERIES MODELS

inferred← citeshttps://doi.org/10.1504/ijmmno.2014.059942openalex-snapshot

Predicting the present with Bayesian structural time series

inferred← citeshttps://doi.org/10.1515/ijb-2025-0011openalex-snapshot

Bayesian competing risks survival modeling for assessing the cause of death of patients with heart failure

inferred← citeshttps://doi.org/10.1561/0800000041openalex-snapshot

Bayesian Approaches to Shrinkage and Sparse Estimation

inferred← citeshttps://doi.org/10.18174/496768openalex-snapshot

Syntheserapport PMR NCV

inferred← citeshttps://doi.org/10.1890/14-0661.1openalex-snapshot

A guide to Bayesian model selection for ecologists

inferred← citeshttps://doi.org/10.3386/w19567openalex-snapshot

Bayesian Variable Selection for Nowcasting Economic Time Series

inferred← citeshttps://doi.org/10.3390/diagnostics12123148openalex-snapshot

Computational Evidence for Laboratory Diagnostic Pathways: Extracting Predictive Analytes for Myocardial Ischemia from Routine Hospital Data

inferred← citeshttps://doi.org/10.3920/978-90-8686-940-4_343openalex-snapshot

343. Subsetted orthogonal data augmentation for fast parallel implementation of Bayesian models for whole-genome analyses

inferred← citeshttps://doi.org/10.48550/arxiv.1410.2392openalex-snapshot

Control functionals for Monte Carlo integration

inferred← citeshttps://doi.org/10.48550/arxiv.1708.05678openalex-snapshot

In Search of Lost (Mixing) Time: Adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p

inferred← citeshttps://doi.org/10.48550/arxiv.1902.03316openalex-snapshot

Bayesian Model Selection with Graph Structured Sparsity

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.1/reviews/1openalex-snapshot

Peer Review #1 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.1/reviews/2openalex-snapshot

Peer Review #2 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.1/reviews/3openalex-snapshot

Peer Review #3 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.1)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.2/reviews/1openalex-snapshot

Peer Review #1 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.2/reviews/2openalex-snapshot

Peer Review #2 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.2/reviews/3openalex-snapshot

Peer Review #3 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.2)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.3/reviews/1openalex-snapshot

Peer Review #1 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.3)"

inferred← citeshttps://doi.org/10.7287/peerj.11537v0.3/reviews/2openalex-snapshot

Peer Review #2 of "Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models (v0.3)"

inferred← citeshttps://doi.org/10.7717/peerj.11537openalex-snapshot

Analysis and forecasts for trends of COVID-19 in Pakistan using Bayesian models

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