State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They...
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Online Access: | https://era.library.ualberta.ca/items/8e66e696-b638-4ead-a4fe-59e4f27694d9 https://doi.org/10.7939/r3-tb84-ww75 |
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ftunivalberta:oai:era.library.ualberta.ca:8e66e696-b638-4ead-a4fe-59e4f27694d9 2024-06-23T07:57:23+00:00 State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems Auger-Méthé, Marie Field, Chris Albertsen, Christopher M. Derocher, Andrew E. Lewis, Mark A. Jonsen, Ian D. Mills Flemming, Joanna 2016-01-01 https://era.library.ualberta.ca/items/8e66e696-b638-4ead-a4fe-59e4f27694d9 https://doi.org/10.7939/r3-tb84-ww75 English eng https://era.library.ualberta.ca/items/8e66e696-b638-4ead-a4fe-59e4f27694d9 doi:10.7939/r3-tb84-ww75 http://creativecommons.org/licenses/by/4.0/ State-Space Models Parameter-Estimation Problems Simulation Study Animal Movement Paths State-Estimation Problems Article (Published) 2016 ftunivalberta https://doi.org/10.7939/r3-tb84-ww75 2024-06-03T03:09:00Z State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results. Article in Journal/Newspaper Ursus maritimus University of Alberta: Era - Education and Research Archive |
institution |
Open Polar |
collection |
University of Alberta: Era - Education and Research Archive |
op_collection_id |
ftunivalberta |
language |
English |
topic |
State-Space Models Parameter-Estimation Problems Simulation Study Animal Movement Paths State-Estimation Problems |
spellingShingle |
State-Space Models Parameter-Estimation Problems Simulation Study Animal Movement Paths State-Estimation Problems Auger-Méthé, Marie Field, Chris Albertsen, Christopher M. Derocher, Andrew E. Lewis, Mark A. Jonsen, Ian D. Mills Flemming, Joanna State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
topic_facet |
State-Space Models Parameter-Estimation Problems Simulation Study Animal Movement Paths State-Estimation Problems |
description |
State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results. |
format |
Article in Journal/Newspaper |
author |
Auger-Méthé, Marie Field, Chris Albertsen, Christopher M. Derocher, Andrew E. Lewis, Mark A. Jonsen, Ian D. Mills Flemming, Joanna |
author_facet |
Auger-Méthé, Marie Field, Chris Albertsen, Christopher M. Derocher, Andrew E. Lewis, Mark A. Jonsen, Ian D. Mills Flemming, Joanna |
author_sort |
Auger-Méthé, Marie |
title |
State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
title_short |
State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
title_full |
State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
title_fullStr |
State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
title_full_unstemmed |
State-space models' dirty little secrets: Even simple linear Gaussian models can have parameter and state estimation problems |
title_sort |
state-space models' dirty little secrets: even simple linear gaussian models can have parameter and state estimation problems |
publishDate |
2016 |
url |
https://era.library.ualberta.ca/items/8e66e696-b638-4ead-a4fe-59e4f27694d9 https://doi.org/10.7939/r3-tb84-ww75 |
genre |
Ursus maritimus |
genre_facet |
Ursus maritimus |
op_relation |
https://era.library.ualberta.ca/items/8e66e696-b638-4ead-a4fe-59e4f27694d9 doi:10.7939/r3-tb84-ww75 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.7939/r3-tb84-ww75 |
_version_ |
1802650998057992192 |