Estimation methods for nonlinear state-space models in ecology

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.
Original languageEnglish
JournalEcological Modelling
Volume222
Issue number8
Pages (from-to)1394-1400
ISSN0304-3800
DOIs
Publication statusPublished - 2011

Cite this

@article{9bc16f45530d484aa22bfcd7afcb517f,
title = "Estimation methods for nonlinear state-space models in ecology",
abstract = "The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.",
author = "Pedersen, {Martin W{\ae}ver} and Berg, {Casper Willestofte} and Thygesen, {Uffe H{\o}gsbro} and Anders Nielsen and Henrik Madsen",
year = "2011",
doi = "10.1016/j.ecolmodel.2011.01.007",
language = "English",
volume = "222",
pages = "1394--1400",
journal = "Ecological Modelling",
issn = "0304-3800",
publisher = "Elsevier",
number = "8",

}

Estimation methods for nonlinear state-space models in ecology. / Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro; Nielsen, Anders; Madsen, Henrik.

In: Ecological Modelling, Vol. 222, No. 8, 2011, p. 1394-1400.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Estimation methods for nonlinear state-space models in ecology

AU - Pedersen, Martin Wæver

AU - Berg, Casper Willestofte

AU - Thygesen, Uffe Høgsbro

AU - Nielsen, Anders

AU - Madsen, Henrik

PY - 2011

Y1 - 2011

N2 - The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.

AB - The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance for all three methods was largely identical, however with BUGS providing overall wider credible intervals for parameters than HMM and ADMB confidence intervals.

U2 - 10.1016/j.ecolmodel.2011.01.007

DO - 10.1016/j.ecolmodel.2011.01.007

M3 - Journal article

VL - 222

SP - 1394

EP - 1400

JO - Ecological Modelling

JF - Ecological Modelling

SN - 0304-3800

IS - 8

ER -