Towards robust statistical inference for complex computer models

Oberpriller, Johannes; Cameron, David R.; Dietze, Michael C.; Hartig, Florian. 2021 Towards robust statistical inference for complex computer models. Ecology Letters, 24 (6). 1251-1261. https://doi.org/10.1111/ele.13728

Before downloading, please read NORA policies.

Preview

Text
N530119JA.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (672kB) | Preview

Official URL: http://dx.doi.org/10.1111/ele.13728

Abstract/Summary

Ecologists increasingly rely on complex computer simulations to forecast ecological systems. To make such forecasts precise, uncertainties in model parameters and structure must be reduced and correctly propagated to model outputs. Naively using standard statistical techniques for this task, however, can lead to bias and underestimation of uncertainties in parameters and predictions. Here, we explain why these problems occur and propose a framework for robust inference with complex computer simulations. After having identified that model error is more consequential in complex computer simulations, due to their more pronounced nonlinearity and interconnectedness, we discuss as possible solutions data rebalancing and adding bias corrections on model outputs or processes during or after the calibration procedure. We illustrate the methods in a case study, using a dynamic vegetation model. We conclude that developing better methods for robust inference of complex computer simulations is vital for generating reliable predictions of ecosystem responses.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1111/ele.13728
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	1461-023X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	Bayesian inference, bias correction, biased models, data imbalance, robust inference.
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	21 Apr 2021 10:04 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530119

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1111/ele.13728

UKCEH and CEH Sections/Science Areas:

Atmospheric Chemistry and Effects (Science Area 2017-)

ISSN:

1461-023X

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

Bayesian inference, bias correction, biased models, data imbalance, robust inference.