# Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors

@article{Qin2018TraceclassMC,
title={Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors},
author={Qian Qin and James P. Hobert},
journal={J. Multivar. Anal.},
year={2018},
volume={166},
pages={335-345}
}
• Published 30 January 2016
• Mathematics
• J. Multivar. Anal.
5 Citations
Convergence Analysis of MCMC Algorithms for Bayesian Multivariate Linear Regression with Non‐Gaussian Errors
• Mathematics
• 2018
When Gaussian errors are inappropriate in a multivariate linear regression setting, it is often assumed that the errors are iid from a distribution that is a scale mixture of multivariate normals.
Uncertainty Quantification for Modern High-Dimensional Regression via Scalable Bayesian Methods
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Journal of Computational and Graphical Statistics
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It is demonstrated that the proposed class of two-step blocked samplers exhibits vastly superior convergence behavior compared to the original three-step sampler in high-dimensional regimes on simulated data as well as data from a variety of applications including gene expression data, infrared spectroscopy data, and socio-economic/law enforcement data.
Consistent estimation of the spectrum of trace class Data Augmentation algorithms
• Mathematics, Computer Science
Bernoulli
• 2019
This paper proposes a novel method to consistently estimate the entire spectrum of a general class of Markov chains arising from a popular and widely used statistical approach known as Data Augmentation.
Estimating the spectral gap of a trace-class Markov operator
• Mathematics
Electronic Journal of Statistics
• 2019
The utility of a Markov chain Monte Carlo algorithm is, in large part, determined by the size of the spectral gap of the corresponding Markov operator. However, calculating (and even approximating)

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