Validation of ecological state space models using the Laplace approximation

Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive adap...

Full description

Saved in:
Bibliographic Details
Published in:Environmental and ecological statistics 2017-06, Vol.24 (2), p.317-339
Main Authors: Thygesen, Uffe Høgsbro, Albertsen, Christoffer Moesgaard, Berg, Casper Willestofte, Kristensen, Kasper, Nielsen, Anders
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Approximation
Biomedical and Life Sciences
Chemistry and Earth Sciences
Computer Science
Computer simulation
Dynamics
Ecological models
Ecology
Health Sciences
Hypothesis testing
Laplace transforms
Life Sciences
Markov analysis
Math. Appl. in Environmental Science
Mathematical models
Medicine
Nonlinear dynamics
Physics
State space models
Statistical analysis
Statistical models
Statistics for Engineering
Statistics for Life Sciences
Theoretical Ecology/Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Many statistical models in ecology follow the state space paradigm. For such models, the important step of model validation rarely receives as much attention as estimation or hypothesis testing, perhaps due to lack of available algorithms and software. Model validation is often based on a naive adaptation of Pearson residuals, i.e. the difference between observations and posterior means, even if this approach is flawed. Here, we consider validation of state space models through one-step prediction errors, and discuss principles and practicalities arising when the model has been fitted with a tool for estimation in general mixed effects models. Implementing one-step predictions in the R package Template Model Builder, we demonstrate that it is possible to perform model validation with little effort, even if the ecological model is multivariate, has non-linear dynamics, and whether observations are continuous or discrete. With both simulated data, and a real data set related to geolocation of seals, we demonstrate both the potential and the limitations of the techniques. Our results fill a need for convenient methods for validating a state space model, or alternatively, rejecting it while indicating useful directions in which the model could be improved.
ISSN:1352-8505
1573-3009
DOI:10.1007/s10651-017-0372-4