Abstract
Environmental scientists face multiple challenges when analyzing unevenly recorded time series with small sample sizes. For example, trends in water temperature may be confounded with time and date of sampling when the latter represent convenience samples and thus introduce bias into regression estimates. We address these concerns using multilevel random slope models and nonparametric bootstrap inference for assessing the statistical significance of the annual trend in river temperature when measurement times and dates are haphazard.
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Acknowledgements
Authors thank anonymous reviewers for their helpful comments and suggestions. The research is supported by the US Army Corps of Engineers – Upper Mississippi River Restoration – Environmental Management Program, Natural Sciences and Engineering Research Council of Canada, and Mitacs Canada.
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Lyubchich, V., Gray, B.R., Gel, Y.R. (2015). Multilevel Random Slope Approach and Nonparametric Inference for River Temperature, Under Haphazard Sampling. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_13
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DOI: https://doi.org/10.1007/978-3-319-17220-0_13
Publisher Name: Springer, Cham
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