Publications

• Mondal, D. and Wang, C. (2018), "A matrix-free method for spatial-temporal Gaussian state-space models," to appear in Statstica Sinica.
• Mondal, D. (2018), "On edge correction of conditional and intrinsic autoregressions," to appear in Biometrika.
• Chaudhuri, S., Mondal, D., and Yin, T. (2017), "Hamiltonian Monte Carlo sampling in Bayesian empirical likelihood computation," Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(1), 293-320.
• Mondal, D. and Hinrichs, N. (2016), "Rank Tests from Partially Ordered Data Using Importance and MCMC Sampling Methods," Statistical Science, 31(3), 325-347.
• Dutta, S. and Mondal, D. (2016), "REML estimation with intrinsic Matérn dependence in the spatial linear mixed model," Electronic Journal of Statistics, 10(2), 2856-2893.
Dutta, S. and Mondal, D. (2015), "An h‐likelihood method for spatial mixed linear models based on intrinsic auto‐regressions," Journal of the Royal Statistical Society: Series B (Statistical Methodology), 77(3), 699-726.
Mondal, D. (2015), "Applying Dynkin’s isomorphism: an alternative approach to understand the Markov property of the de wijs process," Bernoulli, 21(3), 1289-1303.
Besag, J. and Mondal, D. (2013), "Exact Goodness‐of‐Fit Tests for Markov Chains," Biometrics, 69(2), 488-496.
Mondal, D. and Percival, D. B. (2010), "Wavelet variance analysis for gappy time series," Annals of the Institute of Statistical Mathematics, 62(5), 943-966.
Besag, J. and Mondal, D. (2005), "First-order intrinsic autoregressions and the de Wijs process," Biometrika, 92(4), 909-920.