Research interests
Convergence rates of Markov chain Monte Carlo algorithms, model
selection, spatial statistics, survival analysis, Bayes and empirical
Bayes methods
Papers
- 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. and Dey, D. K. (2013) Propriety of posterior distributions arising in categorical and survival models under generalized extreme value distribution Statistica Sinica, to appear
- 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, Stat. and Prob. Letters, 82 103-108
pdf
- 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 pdf
- 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 pdf
- 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 pdf
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