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Abstract The ever increasing popularity of machine learning methods in virtually all areas of science, engineering and beyond is poised to put established statistical modeling approaches into question. Environmental statistics is no exception, as popular constructs such as neural networks and decision trees are now routinely used to provide forecasts of physical processes ranging from air pollution to meteorology. This presents both challenges and opportunities to the statistical community, which could contribute to the machine learning literature with a model‐based approach with formal uncertainty quantification. Should, however, classical statistical methodologies be discarded altogether in environmental statistics, and should our contribution be focused on formalizing machine learning constructs? This work aims at providing some answers to this thought‐provoking question with two time series case studies where selected models from both the statistical and machine learning literature are compared in terms of forecasting skills, uncertainty quantification and computational time. Relative merits of both class of approaches are discussed, and broad open questions are formulated as a baseline for a discussion on the topic.
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New viruses are inevitable; pandemics are optional—Lessons for and from statistics
Abstract We explore ways in which statistics can be used to understand disease spread and support decision‐making by governments. “Past performance does not guarantee future results”—we hope. We discuss and show examples from the National Science Foundation (NSF)‐funded COVID‐Inspired Data Science Education through Epidemiology (CIDSEE) project. Throughout, the emphasis is on the relationships between evidence, modeling and theorizing, and appropriate action. Statistics should be an essential element in all these aspects. We point to some “big statistical ideas” that underpin the whole process of modeling, which can be illustrated vividly in the context of pandemics. We argue that statistics education should emphasize the application of statistics in practical situations, and that many curricula do not equip students to use their understandings of statistics outside the classroom. We offer a framework for curriculum analysis and point to some rich teaching resources.
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- Award ID(s):
- 2313212
- PAR ID:
- 10524345
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Teaching Statistics
- Volume:
- 46
- Issue:
- 3
- ISSN:
- 0141-982X
- Format(s):
- Medium: X Size: p. 132-140
- Size(s):
- p. 132-140
- Sponsoring Org:
- National Science Foundation
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