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Integrating machine learning and Bayesian nonparametrics for flexible modeling of point pattern data

https://doi.org/10.1016/j.csda.2023.107875

Heaton, Matthew J; Dahl, Benjamin K; Dayley, Caleb; Warr, Richard L; White, Philip (March 2024, Computational Statistics & Data Analysis)

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Model-Based Clustering of Trends and Cycles of Nitrate Concentrations in Rivers Across France

https://doi.org/10.1007/s13253-022-00513-2

Heiner, Matthew; Heaton, Matthew J.; Abbott, Benjamin; White, Philip; Minaudo, Camille; Dupas, Rémi (March 2023, Journal of Agricultural, Biological and Environmental Statistics)

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The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime

https://doi.org/10.1029/2023WR034484

Brown, Brian C.; Fullerton, Aimee H.; Kopp, Darin; Tromboni, Flavia; Shogren, Arial J.; Webb, J. Angus; Ruffing, Claire; Heaton, Matthew; Kuglerová, Lenka; Allen, Daniel C.; et al (July 2023, Water Resources Research)

Key Points We compared tools for describing streamflow timeseries, including streamflow metrics, wavelet, and Fourier analysis Each method indicated streamflow data are structured: variability at short timescales is negatively correlated with long timescales Globally, dams were less correlated with streamflow regime than catchment size and climate were
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A Case Study Competition Among Methods for Analyzing Large Spatial Data

https://doi.org/10.1007/s13253-018-00348-w

Heaton, Matthew J.; Datta, Abhirup; Finley, Andrew O.; Furrer, Reinhard; Guinness, Joseph; Guhaniyogi, Rajarshi; Gerber, Florian; Gramacy, Robert B.; Hammerling, Dorit; Katzfuss, Matthias; et al (January 2019, Journal of Agricultural, Biological and Environmental Statistics)

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