IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v222y2011i8p1394-1400.html
   My bibliography  Save this article

Estimation methods for nonlinear state-space models in ecology

Author

Listed:
  • Pedersen, M.W.
  • Berg, C.W.
  • Thygesen, U.H.
  • Nielsen, A.
  • Madsen, H.

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.

Suggested Citation

  • Pedersen, M.W. & Berg, C.W. & Thygesen, U.H. & Nielsen, A. & Madsen, H., 2011. "Estimation methods for nonlinear state-space models in ecology," Ecological Modelling, Elsevier, vol. 222(8), pages 1394-1400.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:8:p:1394-1400
    DOI: 10.1016/j.ecolmodel.2011.01.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380011000299
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2011.01.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Guiming, 2007. "On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models," Ecological Modelling, Elsevier, vol. 200(3), pages 521-528.
    2. Wolfinger, Russell D. & Xihong Lin, 1997. "Two Taylor-series approximation methods for nonlinear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 25(4), pages 465-490, September.
    3. Skaug, Hans J. & Fournier, David A., 2006. "Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 699-709, November.
    4. Gimenez, Olivier & Rossi, Vivien & Choquet, Rémi & Dehais, Camille & Doris, Blaise & Varella, Hubert & Vila, Jean-Pierre & Pradel, Roger, 2007. "State-space modelling of data on marked individuals," Ecological Modelling, Elsevier, vol. 206(3), pages 431-438.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2017. "Reprint of: Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 359(C), pages 461-467.
    2. Simone Vincenzi & Marc Mangel & Alain J Crivelli & Stephan Munch & Hans J Skaug, 2014. "Determining Individual Variation in Growth and Its Implication for Life-History and Population Processes Using the Empirical Bayes Method," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-16, September.
    3. Zheng, Nan & Cadigan, Noel, 2021. "Frequentist delta-variance approximations with mixed-effects models and TMB," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Solbu, Erik Blystad & Engen, Steinar & Diserud, Ola Håvard, 2015. "Guidelines when estimating temporal changes in density dependent populations," Ecological Modelling, Elsevier, vol. 313(C), pages 355-376.
    5. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    6. de Ávila-Simas, Sunshine & Morato, Marcelo M. & Reynalte-Tataje, David A. & Silveira, Hector B. & Zaniboni-Filho, Evoy & E. Normey-Rico, Julio, 2019. "Model-based predictive control for the regulation of the golden mussel Limnoperna fortunei (Dunker, 1857)," Ecological Modelling, Elsevier, vol. 406(C), pages 84-97.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kristensen, Kasper & Nielsen, Anders & Berg, Casper W. & Skaug, Hans & Bell, Bradley M., 2016. "TMB: Automatic Differentiation and Laplace Approximation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i05).
    2. Sigourney, Douglas B. & Munch, Stephan B. & Letcher, Benjamin H., 2012. "Combining a Bayesian nonparametric method with a hierarchical framework to estimate individual and temporal variation in growth," Ecological Modelling, Elsevier, vol. 247(C), pages 125-134.
    3. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2013. "Modelling the interindividual variability of organogenesis in sugar beet populations using a hierarchical segmented model," Ecological Modelling, Elsevier, vol. 263(C), pages 56-63.
    4. Solbu, Erik Blystad & Engen, Steinar & Diserud, Ola Håvard, 2015. "Guidelines when estimating temporal changes in density dependent populations," Ecological Modelling, Elsevier, vol. 313(C), pages 355-376.
    5. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    6. Lee, Woojoo & Lim, Johan & Lee, Youngjo & del Castillo, Joan, 2011. "The hierarchical-likelihood approach to autoregressive stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 248-260, January.
    7. Manuel Arias-Rodil & Fernando Castedo-Dorado & Asunción Cámara-Obregón & Ulises Diéguez-Aranda, 2015. "Fitting and Calibrating a Multilevel Mixed-Effects Stem Taper Model for Maritime Pine in NW Spain," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
    8. Kumbhakar, Subal C. & Tsionas, Efthymios G., 2005. "Measuring technical and allocative inefficiency in the translog cost system: a Bayesian approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 355-384, June.
    9. Bellio, Ruggero & Grassetti, Luca, 2011. "Semiparametric stochastic frontier models for clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 71-83, January.
    10. Hans J. Skaug & Jun Yu, 2007. "Automated Likelihood Based Inference for Stochastic Volatility Models," Working Papers CoFie-01-2007, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    11. Hefley, Trevor J. & Tyre, Andrew J. & Blankenship, Erin E., 2013. "Fitting population growth models in the presence of measurement and detection error," Ecological Modelling, Elsevier, vol. 263(C), pages 244-250.
    12. Barker, Daniel & Sibly, Richard M., 2008. "The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation," Ecological Modelling, Elsevier, vol. 219(1), pages 170-177.
    13. Devin S. Johnson & Brian M. Brost & Mevin B. Hooten, 2022. "Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(2), pages 382-400, June.
    14. Meritxell Genovart & Roger Pradel, 2019. "Transience effect in capture-recapture studies: The importance of its biological meaning," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    15. Vila, Jean-Pierre, 2012. "Enhanced consistency of the Resampled Convolution Particle Filter," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 786-797.
    16. William H. Aeberhard & Eva Cantoni & Chris Field & Hans R. Künsch & Joanna Mills Flemming & Ximing Xu, 2021. "Robust estimation for discrete‐time state space models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1127-1147, December.
    17. Skaug, Hans J. & Yu, Jun, 2014. "A flexible and automated likelihood based framework for inference in stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 642-654.
    18. Veyssiere, Luc Pierre, 2009. "A three essays dissertation on agricultural and environmental microeconomics," ISU General Staff Papers 200901010800001958, Iowa State University, Department of Economics.
    19. Tore Selland Kleppe & Hans J. Skaug, 2008. "Building and Fitting Non‐Gaussian Latent Variable Models via the Moment‐Generating Function," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 664-676, December.
    20. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:222:y:2011:i:8:p:1394-1400. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.