R. Majumder and B. J. Reich (2023). A deep learning synthetic likelihood approximation of a non-stationary spatial model for extreme streamflow forecasting. Spatial Statistics, 55:100755.
R. Majumder, B. J. Reich, and B. A. Shaby (2022). Modeling extremal streamflow using deep learning approximations and a flexible spatial process. (Under review) Annals of Applied Statistics.
S. G. Xu, R. Majumder, and B. J. Reich (2022). SPQR: An R Package for semi-parametric density and quantile regression. arXiv.
S. Xu and R. Majumder (2022). SPQR: Semi-Parametric Quantile Regression. R package version 0.1.0.
R. Majumder, Q. Ji, and N.K. Neerchal (2022). Optimal stock portfolio selection with a multivariate hidden Markov model. Sankhya B.
R. Majumder, M. K. Gobbert, and N. K. Neerchal (2021). A modified minibatch sampling method for parameter estimation in hidden Markov models using stochastic variational Bayes. Proc. Appl. Math. Mech., 21(1):e202100203.
G. C. Kroiz, R. Majumder, N. K. Neerchal, M. K. Gobbert, A. Mehta, and K. Markert (2020). Daily precipitation generation using a hidden Markov model with correlated emissions for the Potomac river basin. Proc. Appl. Math. Mech., 20(1):e202000117.
J. X. Xie, X. Fan, C. A. Drummond, R. Majumder, Y. Xie, T. Chen, L. Liu, S. T. Haller, P. S. Brewster, L. D. Dworkin, C. J. Cooper, and J. Tian (2017). MicroRNA profiling in kidney disease: Plasma versus plasma-derived exosomes. Gene, 627:1–8.