Research interests

Papers

• Roy, V. (2024) A geometric approach to informed MCMC sampling, arXiv, R package



• Roy, V., Khare, K., and Hobert, J. P. (2024) The data augmentation algorithm, arXiv



• Roy, V., and Evangelou, E. (2024) Selection of proposal distributions for multiple importance sampling, Statistica Sinica, 34 , 27-46, arXiv



• Rao, Y. and Roy, V. (2023) Necessary and sufficient conditions for posterior propriety for generalized linear mixed models, arXiv



• Brofos, J., Roy, V. and Lederman, R. (2023) Geometric ergodicity in modified variations of Riemannian Manifold and Lagrangian Monte Carlo arXiv



• Roy, V. and Zhang, L. (2023) Convergence of position-dependent MALA with application to conditional simulation in GLMMs, Journal of Computational and Graphical Statistics, 32 , 501-512, arXiv



• Li, D., Dutta, S., and Roy, V. (2023) Model based screening embedded Bayesian variable selection for ultra-high dimensional settings, Journal of Computational and Graphical Statistics, 32 , 61-73, arXiv



• Roy, V. (2022) MCMC for GLMMs, Handbook of Statistics Volume 47: Advancements in Bayesian Methods and Implementation , C.R. Rao, Arni S.R.S. Rao and Alastair Young, eds., Elsevier, 135--159 arXiv



• Dixit, A. and Roy, V. (2022) Analyzing Relevance Vector Machines using a single penalty approach Statistical Analysis and Data Mining, 15 , 143-155, arXiv



• Wang, R., Dutta, S. and Roy, V. (2021) Bayesian iterative screening in ultra-high dimensional settings arXiv



• Rao, Y. and Roy, V. (2021) Block Gibbs samplers for logistic mixed models: convergence properties and a comparison with full Gibbs samplers, Electronic Journal of Statistics, 15 , 5598-5625, arXiv



• Dixit, A. and Roy, V. (2021) Posterior impropriety of some sparse Bayesian learning models Statistics and Probability Letters , 171 109039 arXiv



• Wang, R., Dutta, S. and Roy V. (2021) A note on marginal correlation based screening Statistical Analysis and Data Mining, 14 88-92 arXiv
  
  
• Roy, V. (2020) Convergence diagnostics for Markov chain Monte Carlo Annual Review of Statistics and Its Application, 7 387-412
  an older version is at arXiv
  
  
• Evangelou, E., and Roy, V., (2019) Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling Spatial Statistics, 29 289-315 arXiv
  
  
• Wang, X., and Roy, V. (2018) Geometric ergodicity of Polya-Gamma Gibbs sampler for Bayesian logistic regression with a flat prior Electronic Journal of Statistics, 12 3295--3311 arXiv
  
  
• Wang, X., and Roy, V. (2018) Analysis of the P\'olya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models, Statistics and Probability Letters, 137 251--256 arXiv
  
  
• Wang, X., and Roy, V. (2018) Convergence analysis of the block Gibbs sampler for Bayesian probit linear mixed models with improper priors, Electronic Journal of Statistics 12 4412--4439 arXiv
  
  
• Wang, X., Roy, V. and Zhu Z. (2018) A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach, Statistics and Computing, 28 1083--1094
  
  
• Roy, V., Tan, A. , and Flegal, J. (2018) Estimating standard errors for importance sampling estimators with multiple Markov chains, Statistica Sinica, 28 1079--1101 arXiv
  
  
• Roy, V., and Chakraborty, S. (2017) Selection of tuning parameters, solution paths and standard errors for Bayesian lassos, Bayesian Analysis , 12 753--778
  
  
• Simpson, M. , J. Niemi, and Roy, V. (2017) Interweaving Markov chain Monte Carlo strategies for efficient estimation of dynamic linear models, Journal of Computational and Graphical Statistics, 26 152--159
  
  
• Dixit, A. and Roy, V. (2017) MCMC diagnostics for higher dimensions using Kullback Leibler divergence, Journal of Statistical Computation and Simulation, 87 2622--2638
  
  
• Laha, A. , Dutta, S. and Roy V. (2017) A novel sandwich algorithm for empirical Bayes analysis of rank data, Statistics and its Interface, 10 543--556
  
  
• Athreya, K. B. and Roy, V. (2016) General Glivenko-Cantelli theorems, Stat , 5 306--311
  
  
• Roy, V. (2016) Improving efficiency of data augmentation algorithms using Peskun's theorem, Computational Statistics, 31 709--728
  
  
• Roy, V., Evangelou, E. and Zhu Z. (2016) Efficient estimation and prediction for the Bayesian binary spatial model with flexible link functions, Biometrics, 72 289--298
  
  
• Roy, D., Roy, V., and Dey, D. K. (2015) Analysis of bivariate survival data based on copulas with log generalized extreme value marginals, Extreme Value Modeling and Risk Analysis: Methods and Applications D. K. Dey and J. Yan, eds. Chapman \& Hall/CRC Press, 475--492
  
  
• Athreya, K. B., Normand, R., Roy, V., and Wu, S.-J. (2015) Limit theorems for the estimation of L1 integrals using the Brownian motion, Statistics and Probability Letters, 100 42--47
  
  
• Athreya, K. B. and Roy, V. (2015) Estimation of integrals with respect to infinite measures using regenerative sequences, Journal of Applied Probability, 52 1133--1145
  
  
• Roy, V., Evangelou, E. and Zhu Z. (2015) Empirical Bayes methods for the transformed Gaussian random field model with additive measurement errors, Current Trends in Bayesian Methodology with Applications, S. K. Upadhyay, U. Singh, D. K. Dey and A. Loganathan, eds. Chapman & Hall/CRC Press, 521--536
  
  
• Athreya, K. B. and Roy, V. (2014) Monte Carlo methods for improper target distributions, Electronic Journal of Statistics, 8 2664-2692
  
  
• Roy, V. (2014) Efficient estimation of the link function parameter in a robust Bayesian binary regression model, Computational Statistics and Data Analysis, 73 87-102
  
  
• Athreya, K. B. and Roy, V. (2014) When is a Markov chain regenerative?, Statistics and Probability Letters, 84 22-26
  
  
• Roy, V. and Dey, D. K. (2014) Propriety of posterior distributions arising in categorical and survival models under generalized extreme value distribution, Statistica Sinica, 24 699-722
  
  
• Roy, V. and Kaiser, M. S. (2013) Posterior propriety for Bayesian binomial regression models with a parametric family of link functions, Statistical Methodology, 13 25-41
  
  
• Roy, V. (2012) Convergence rates for MCMC algorithms for a robust Bayesian binary regression model, Electronic Journal of Statistics, 6 2463-2485
  
  
• Roy, V. (2012) Spectral analytic comparisons for Data Augmentation, Statistics and Probability Letters, 82 103-108
  
  
• Hobert, J. P. ,Roy, V. and Robert, C. P.(2011). Improving the convergence properties of the data augmentation algorithm with an application to Bayesian mixture modelling, Statistical Science , 26 332-351
  
  
• Roy, V. and Hobert, J. P. (2010) On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors, Journal of Multivariate Analysis, 101 1190-1202

• Roy, V. and Hobert, J. P. (2007) Convergence rates and asymptotic standard errors for MCMC algorithms for Bayesian probit regression, Journal of the Royal Statistical Society, Series B , 69 607-623

Interdisciplinary research

• Zheng, Z., Guo, B., Dutta, S., Roy, V., Liu, H., and Schnable, P. S. (2023) The 2020 derecho revealed limited overlap between maize genes associated with root lodging and root system architecture, Plant Physiology

Unpublished Manuscript

• Roy, D., Roy, V. and Dey D. K. (2014) Bayesian analysis of survival data under generalized extreme value distribution with application in cure rate model, Technical Report 49, Department of Statistics, University of Connecticut

Invited Book Review

• Roy, V. (2012) "Handbook of Markov chain Monte Carlo" edited by S. P. Brooks, A. Gelman, G. L. Jones and X.-L. Meng, Journal of the American Statistical Association, 107 434-435