# ./ongoing

- 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.
**R. Majumder**, B. A. Shaby, B. J. Reich, and D.S. Cooley (2024). Semiparametric estimation of the shape of the limiting bivariate point cloud. *arXiv:2306.13257*.
- 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

- 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, developed by Steven G. Xu and me. [GitHub]