Abstract
processes that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argos
satellite tracking data, we demonstrate that the use of continuous-time t-distributed measurement errors for error-prone data is more robust to outliers and improves the location estimation compared to using discretized-time t-distributed errors (implemented with a Gibbs sampler) or
using continuous-time Gaussian errors (as with the Kalman filter). Using TMB, we are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack.
| Original language | English |
|---|---|
| Journal | Ecology |
| Volume | 96 |
| Issue number | 10 |
| Pages (from-to) | 2598-2604 |
| ISSN | 0012-9658 |
| Publication status | Published - 2015 |
Cite this
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Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder. / Albertsen, Christoffer Moesgaard; Whoriskey, Kim; Yurkowski, David; Nielsen, Anders; Flemming, Joanna Mills.
In: Ecology, Vol. 96, No. 10, 2015, p. 2598-2604.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Fast fitting of non-Gaussian state-space models to animal movement data via Template Model Builder
AU - Albertsen, Christoffer Moesgaard
AU - Whoriskey, Kim
AU - Yurkowski, David
AU - Nielsen, Anders
AU - Flemming, Joanna Mills
PY - 2015
Y1 - 2015
N2 - State-space models (SSM) are often used for analyzing complex ecologicalprocesses that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argossatellite tracking data, we demonstrate that the use of continuous-time t-distributed measurement errors for error-prone data is more robust to outliers and improves the location estimation compared to using discretized-time t-distributed errors (implemented with a Gibbs sampler) orusing continuous-time Gaussian errors (as with the Kalman filter). Using TMB, we are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack.
AB - State-space models (SSM) are often used for analyzing complex ecologicalprocesses that are not observed directly, such as marine animal movement. When outliers are present in the measurements, special care is needed in the analysis to obtain reliable location and process estimates. Here we recommend using the Laplace approximation combined with automatic differentiation (as implemented in the novel R package Template Model Builder; TMB) for the fast fitting of continuous-time multivariate non-Gaussian SSMs. Through Argossatellite tracking data, we demonstrate that the use of continuous-time t-distributed measurement errors for error-prone data is more robust to outliers and improves the location estimation compared to using discretized-time t-distributed errors (implemented with a Gibbs sampler) orusing continuous-time Gaussian errors (as with the Kalman filter). Using TMB, we are able to estimate additional parameters compared to previous methods, all without requiring a substantial increase in computational time. The model implementation is made available through the R package argosTrack.
M3 - Journal article
VL - 96
SP - 2598
EP - 2604
JO - Ecology
JF - Ecology
SN - 0012-9658
IS - 10
ER -