Open Collections

Gilson, Liam

University of British Columbia

In applied ecology, especially in fields related to forest ecology, there is a need to make long-term model projections under predicted future conditions. Present methods for model selection do not emphasize reducing risk in the setting of extrapolation, and are often inappropriate for use in a setting where the distribution of predictor variables changes between training and testing (predictive) settings. A method proposed for a domain generalization setting is extended to include linear mixed-effect models, a common class of models used in ecological settings. The invariant models method is based on selecting models which produce independent residuals across tasks, assuming that this conditional independence will also apply to the test task. The performance of this proposed method is evaluated using simulations and an example forest ecology dataset.

Text

2023-09-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85769

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University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/