Sandwich algorithms for Bayesian variable selection
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Publication:1623728
DOI10.1016/j.csda.2014.07.014OpenAlexW2172242643MaRDI QIDQ1623728
Publication date: 23 November 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.csda.2014.07.014
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- On Bayesian model and variable selection using MCMC
- A spectral analytic comparison of trace-class data augmentation algorithms and their sandwich variants
- Gibbs sampling, exponential families and orthogonal polynomials
- Geometric ergodicity and hybrid Markov chains
- Ordering and improving the performance of Monte Carlo Markov chains.
- Model uncertainty
- A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms
- Markov Chains and De-initializing Processes
- Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach
- Elastic Net Regression Modeling With the Orthant Normal Prior
- The Calculation of Posterior Distributions by Data Augmentation
- Seeking efficient data augmentation schemes via conditional and marginal augmentation
- Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
- Prediction Via Orthogonalized Model Mixing
- Bayesian Wavelet Regression on Curves With Application to a Spectroscopic Calibration Problem
- Asymptotic Variance and Convergence Rates of Nearly-Periodic Markov Chain Monte Carlo Algorithms
- Parameter Expansion for Data Augmentation
- Shotgun Stochastic Search for “Largep” Regression
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