Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

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SEEK ID: https://publications.h-its.org/publications/1691

DOI: 10.1016/j.ijforecast.2022.06.005

Projects: Computational Statistics

Publication type: Journal

Journal: International Journal of Forecasting

Publisher: Elsevier BV

Citation: International Journal of Forecasting,39(3):1366–1383

Date Published: 1st Jul 2023

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Registered Mode: manually

Authors: Evan L. Ray, Logan C. Brooks, Jacob Bien, Matthew Biggerstaff, Nikos I. Bosse, Johannes Bracher, Estee Y. Cramer, Sebastian Funk, Aaron Gerding, Michael A. Johansson, Aaron Rumack, Yijin Wang, Martha Zorn, Ryan J. Tibshirani, Nicholas G. Reich

Citation
Ray, E. L., Brooks, L. C., Bien, J., Biggerstaff, M., Bosse, N. I., Bracher, J., Cramer, E. Y., Funk, S., Gerding, A., Johansson, M. A., Rumack, A., Wang, Y., Zorn, M., Tibshirani, R. J., & Reich, N. G. (2023). Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States. In International Journal of Forecasting (Vol. 39, Issue 3, pp. 1366–1383). Elsevier BV. https://doi.org/10.1016/j.ijforecast.2022.06.005
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Created: 10th Aug 2023 at 11:54

Last updated: 10th Aug 2023 at 11:56

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