Léo R. Belzile, Arnab Hazra, and **Rishikesh Yadav (2024). An utopic adventure in the modeling of conditional univariate and multivariate extremes. Accepted for Publication in Journal Extremes. arXiv preprint
*Arnab Hazra, *Pratik Nag, *Rishikesh Yadav, and Ying Sun (2024). Exploring the efficacy of statistical and deep learning methods for large spatial datasets: A Case Study. Journal of Agricultural, Biological and Environmental Statistics. https://doi.org/10.1007/s13253-024-00602-4
Rishikesh Yadav, Raphael Huser, Thomas Opitz, and Luigi Lombardo (2023). Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions. Journal of the Royal Statistical Society Series C (JRSSC): Applied Statistics, qlad077. https://doi.org/10.1093/jrsssc/qlad077
Rishikesh Yadav, Raphael Huser, and Thomas Opitz(2022). A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data. Spatial Statistics 51, 100672. https://doi.org/10.1016/j.spasta.2022.100672
*Daniela Cisneros, *Yan Gong, *Rishikesh Yadav, Arnab Hazra, and Raphael Huser(2022). A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes. Extremes, 1-30. https://doi.org/10.1007/s10687-022-00460-8
Rishikesh Yadav, Raphael Huser, and Thomas Opitz (2021). Spatial hierarchical modeling of threshold exceedances using rate mixtures. Environmetrics 32 (3), e2662. https://doi.org/10.1002/env.2662
* Equal contribution; ** Contributed in three out of four sub-tasks