./ongoing

  • Yan Gong, Reetam Majumder, Brian J. Reich, Raphaël Huser (2025). Causal spatial quantile regression. arXiv:2509.02294.
  • R. Majumder and Jordan Richards (2025). Semi-parametric bulk and tail regression using spline-based neural networks. arXiv:2504.19994.
  • R. Majumder, S. Fang, A. Sankarasubramanian, E.C. Hector, B.J. Reich (2024+). Spatiotemporal density ncorrection of multivariate global climate model projections using deep learning. arXiv:2411.18799.
  • B. Feng, R. Majumder, B. J. Reich, M. A. Abba (2024). Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes. arXiv:2411.06324.
  • C. J. R. Murphy-Barltrop, R. Majumder, and J. Richards (2024). Deep learning of multivariate extremes via a geometric representation. arXiv:2406.19936.
  • M. A. Abba, B. J. Reich, R. Majumder, and B. Feng (2024). Stochastic gradient MCMC for massive geostatistical data. arXiv:2405.04531.
  • R. Majumder, B. A. Shaby, and B. J. Reich (2024). Introduction to Bayesian methods of extreme value analysis. In M. de Carvalho, R. Huser, P. Naveau, and B. J. Reich (Eds.), Handbook on Statistics of Extremes, to appear.
  • S. G. Xu, R. Majumder, and B. J. Reich (2022). SPQR: An R Package for Semi-Parametric Density and Quantile Regression. arXiv:2210.14482.

./peer reviewed

  • S. Fang, R. Majumder, E.J. Hector, B.J. Reich, A. Sankarasubramanian (2025). A complete density correction using normalizing flows (CDC-NF) for CMIP6 GCMs. Scientific Data, 12:1279.
  • R. Majumder, B. A. Shaby, B. J. Reich, and D.S. Cooley (2025). Semiparametric estimation of the shape of the limiting bivariate point cloud. Bayesian Analysis, Bayesian Anal. Advance Publication, 1-27.
  • R. Majumder, A. J. Terando, J. K. Hiers, J. A. Collazo, B. J. Reich (2025). A spatiotemporal optimization engine for prescribed burning in the Southeast US. Ecological Informatics, 85:102956.
  • A. Russell, N. Fontana, T. Hoecker, A. Kamanu, R. Majumder, J. Stephens, A. M. Young, A.E. Cravens, C. Giardina, J. K. Hiers, J. Littell, and A. J. Terando (2024). A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation. Cell Reports Sustainability, 1(6):100125.
  • R. Majumder, B. J. Reich, and B. A. Shaby (2024). Modeling extremal streamflow using deep learning approximations and a flexible spatial process. Annals of Applied Statistics, 18(2): 1519-1542.
  • 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, Q. Ji, and N.K. Neerchal (2023). Optimal stock portfolio selection with a multivariate hidden Markov model. Sankhya B, 85 (Suppl 1), 177-198.
  • 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.

./conference proceedings

  • 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.

./software

  • BezELS: Bezier splines for Estimating Limit Sets. [Github]
  • SPQR: Semi-parametric quantile regression (with Steven G. Xu). [GitHub]
  • spSGMCMC: spatial Stochastic Gradient Markov Chain Monte Carlo sampling (with Mohamed A. Abba). [GitHub]