Citations and References

Citation

If you use nsEVDx in your research, please cite the following:

  • Kafle, N., & Meier, C. I. (2025). nsEVDx: A Python library for modeling Non-Stationary Extreme Value Distributions. arXiv preprint arXiv:2509.07261.

  • Kafle, N., & Meier, C. (2025). nsEVDx: A Python Library for Modeling Non-Stationary Extreme Value Distributions (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.21286163

References

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  • Betancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv: Methodology.

  • Castillo, E. (1988). Extreme Value Theory in Engineering. Academic Press.

  • Coles, S. (2007). An introduction to statistical modeling of extreme values. Springer. https://doi.org/10.1007/978-1-4471-3675-0

  • Eccles, R., Syktus, J., Trancoso, R., Chapman, S., Wasko, C., Evans, J. P., Thatcher, M., Di Virgilio, G., & Stassen, C. (2025). Substantial increases in future precipitation extremes—Insights from a large ensemble of downscaled CMIP6 models. npj Natural Hazards, 2(1), 60. https://doi.org/10.1038/s44304-025-00107-1

  • Gilleland, E. (2025). extRemes: Extreme Value Analysis. https://doi.org/10.32614/CRAN.package.extRemes

  • Heffernan, J. E., Stephenson, A. G., & Gilleland, E. (2003). ismev: An introduction to statistical modeling of extreme values. https://CRAN.R-project.org/package=ismev

  • Hosking, J. R. M., & Wallis, J. R. (1997). Regional frequency analysis: An approach based on L-moments. Cambridge University Press. https://doi.org/10.1017/CBO9780511529443

  • Jayaweera, L., Wasko, C., & Nathan, R. (2025). Evidence for non-stationarity in the GEV shape parameter when modeling extreme rainfall. Water Resources Research, 61(5), e2023WR036426. https://doi.org/10.1029/2023WR036426

  • Kafle, N. (2026). Rain-Gauge Network Effects on the Uncertainty and Trends in Short-Duration Extreme Precipitation. Doctoral dissertation, The University of Memphis.

  • Kafle, N., & Meier, C. I. (n.d.). Detecting trends in short-duration extreme precipitation over SEUS using neighborhood-based method. Manuscript in preparation.

  • Kafle, N., & Meier, C. (2025). Evaluating Methodologies for Detecting Trends in Short-Duration Extreme Rainfall in the Southeastern United States. Extreme Hydrological or Critical Event Analysis-III, EWRI Congress 2025, Anchorage, AK, U.S. https://alaska2025.eventscribe.net

  • Kafle, N., Dell’Aira, F., Chadwick, C., & Meier, C. I. (2026). Robustness of regionally derived, short-duration rainfall depth-duration-frequency estimates to the choice of minimum interevent time: Evidence across climates, raingauge densities, and regionalization approaches. Journal of Hydrologic Engineering. https://doi.org/10.1061/JHYEFF/HEENG-6729

  • Kafle, N., Peleg, N., & Meier, C. I. (2025). Detecting spatially consistent trends in sub-hourly extreme rainfall using a neighborhood-based method. AGU Fall Meeting Abstracts, H13G-07. https://ui.adsabs.harvard.edu/abs/2025AGUFMH13G…07K/abstract

  • Katz, R. W., Parlange, M. B., & Naveau, P. (2002). Statistics of extremes in hydrology. Advances in Water Resources, 25(8–12), 1287–1304. https://doi.org/10.1016/S0309-1708(02)00056-8

  • McNeil, A. J., & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach. Journal of Empirical Finance, 7(3–4), 271–300. https://doi.org/10.1016/S0927-5398(00)00012-8

  • Paciorek, C. (2016). climextRemes: Tools for analyzing climate extremes. https://CRAN.R-project.org/package=climextRemes

  • Phan, D., Pradhan, N., & Jankowiak, M. (2019). Composable effects for flexible and accelerated probabilistic programming in NumPyro. arXiv. https://doi.org/10.48550/arXiv.1912.11554

  • Prosdocimi, I., Kjeldsen, T. R., & Miller, J. D. (2015). Detection and attribution of urbanization effect on flood extremes using nonstationary flood-frequency models. Water Resources Research, 51(6), 4244–4262. https://doi.org/10.1002/2015WR017065

  • Robert, C. P., & Casella, G. (2009). Introducing Monte Carlo Methods with R. Springer. https://doi.org/10.1007/978-1-4419-1576-4

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  • Stan Development Team. (2023a). CmdStan: The command-line interface to Stan. https://mc-stan.org/users/interfaces/cmdstan

  • Stan Development Team. (2023b). PyStan: The Python interface to Stan. https://pystan.readthedocs.io/

  • Deville, Y. (2026). NSGEV: Non-Stationary GEV Time Series (Version 0.2.3). IRSN. https://github.com/IRSN/NSGEV/