Preprint

  1. Estimating Monte Carlo variance from multiple Markov chains Gupta, Kushagra, and Vats, Dootika Preprint [arXiv] [Code]
  2. Lugsail lag windows and their application to MCMC Vats, Dootika, and Flegal, James M Preprint [arXiv]
  3. Efficient Bernoulli factory MCMC for intractable likelihoods Vats, Dootika, Gonçalves, Flávio B, Łatuszyński, Krzysztof, and Roberts, Gareth O Preprint [arXiv] [Code]
  4. New visualizations for Monte Carlo simulations Robertson, Nathan, Flegal, James M, Jones, Galin L, and Vats, Dootika Preprint [arXiv]
  5. Batch size selection for variance estimators in MCMC Liu, Ying, Vats, Dootika, and Flegal, James M Preprint [arXiv]
  6. Revisiting the Gelman-Rubin diagnostic Vats, Dootika, and Knudson, Christina Preprint [arXiv]

2020

  1. Analyzing Markov chain Monte Carlo output Vats, Dootika, Robertson, Nathan, Flegal, James M, and Jones, Galin L WIREs Computational Statistics, to appear. 2020 [arXiv] [HTML]
  2. Comment: "Unbiased Markov chain Monte Carlo with couplings" by Jacob et. al. Vats, Dootika, and Jones, Galin L Journal of the Royal Statistical Society, Series B 2020 [HTML]

2019

  1. Multivariate output analysis for Markov chain Monte Carlo Vats, Dootika, Flegal, James M, and Jones, Galin L Biometrika 2019 [arXiv] [HTML]

2018

  1. Strong Consistency of Multivariate Spectral Variance Estimators in Markov chain Monte Carlo Vats, Dootika, Flegal, James M, and Jones, Galin L Bernoulli 2018 [arXiv] [HTML]

2017

  1. Geometric ergodicity of Gibbs samplers in Bayesian penalized regression models Vats, Dootika Electronic Journal of Statistics 2017 [arXiv] [HTML]