Estimation methods for nonlinear state-space models in ecology

Ecological Modelling

Volumn 222, Issue 8, 2011, Pages 1394-1400

  Pedersen, M W ^a   Berg, C W ^a   Thygesen, U H ^a   Nielsen, A ^a   Madsen, H ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

AD Model Builder; Hidden Markov model; Mixed model; Monte Carlo; Theta logistic population model; WinBUGS

Indexed keywords

AD MODEL BUILDER; MIXED MODEL; MONTE CARLO; THETA LOGISTIC POPULATION MODEL; WINBUGS;

ECOLOGY; HIDDEN MARKOV MODELS; MONTE CARLO METHODS; PARAMETER ESTIMATION;

NUMERICAL METHODS;

BAYESIAN ANALYSIS; BENCHMARKING; DATA SET; ECOLOGICAL MODELING; ESTIMATION METHOD; MONTE CARLO ANALYSIS; NONLINEARITY; NUMERICAL MODEL; PARAMETERIZATION; POPULATION DYNAMICS; POPULATION MODELING; SOFTWARE;

EID: 79953029713     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2011.01.007     Document Type: Article

References (29)

1. ADMB-project, 2009a. AD Model Builder: Automatic Differentiation Model Builder. Developed by David Fournier and freely available from admb-project.org.
2. ADMB-project Random Effects in AD Model Builder: ADMB-RE User Guide 2009.
3. Cappé O., Moulines E., Ryden T. Inference in Hidden Markov Models 2005, Springer.
4. Casella G., George E. Explaining the Gibbs sampler. American Statistician 1992, 46(3):167-174.
5. Clark F., Brook B., Delean S., Reşit Akçakaya H., Bradshaw C. The theta-logistic is unreliable for modelling most census data. Methods in Ecology and Evolution 2010, 1(3):253-262.
6. de Valpine P., Hastings A. Fitting population models incorporating process noise and observation error. Ecological Monographs 2002, 72:57-76.
7. Gamerman D. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing 1997, 7(1):57-68.
8. Gelman A. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 2006, 1(3):515-533.
9. Geweke J. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Bayesian Statistics 1992, vol. 4:169-193. Clarendon Press, Oxford, UK.
10. Gimenez O., Bonner S., King R., Parker R., Brooks S., Jamieson L., Grosbois V., Morgan B., Thomas L. WinBUGS for population ecologists: Bayesian modeling using Markov Chain Monte Carlo methods. Modeling Demographic Processes In Marked Populations. Vol. 3 of Mathematics and Statistics 2008, 883-915. Springer, US. D.L. Thomson, E.G. C., M.J. Conroy (Eds.).
11. Gimenez O., Rossi V., Choquet R., Dehais C., Doris B., Varella H., Vila J., Pradel R. State-space modelling of data on marked individuals. Ecological Modelling 2007, 206(3-4):431-438.
12. Jonsen I.D., Flemming J.M., Myers R.A. Robust state-space modeling of animal movement data. Ecology 2005, 86(11):2874-2880.
13. Kitagawa G. Non-Gaussian state-space modeling of nonstationary time series. Journal of the American Statistical Association 1987, 82(400):1032-1041.
14. Lambert P., Sutton A., Burton P., Abrams K., Jones D. How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS. Statistics in Medicine 2005, 24(15):2401-2428.
15. Madsen H. Time Series Analysis 2008, Chapman & Hall/CRC London, London.
16. Madsen H., Thyregod P. Introduction to General and Generalized Linear Models 2010, Chapman & Hall/CRC, London.
17. Morales J., Haydon D., Frair J., Holsinger K., Fryxell J. Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 2004, 85(9):2436-2445.
18. Mortensen, S.B., 2009. Markov and mixed models with applications. Ph.D. Thesis, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark.
19. Oehlert G.W. A note on the delta method. American Statistician 1992, 46(1):27-29.
20. Patterson T., Thomas L., Wilcox C., Ovaskainen O., Matthiopoulos J. State-space models of individual animal movement. Trends in Ecology & Evolution 2008, 23(2):87-94.
21. Polansky L., De Valpine P., Lloyd-Smith J., Getz W. Likelihood ridges and multimodality in population growth rate models. Ecology 2009, 90(8):2313-2320.
22. Schofield M., Barker R., MacKenzie D. Flexible hierarchical mark-recapture modeling for open populations using WinBUGS. Environmental and Ecological Statistics 2009, 16(3):369-387.
23. Skaug H., Fournier D. Automatic approximation of the marginal likelihood in non-gaussian hierarchical models. Computational Statistics & Data Analysis 2006, 51(2):699-709.
24. Spiegelhalter D., Thomas A., Best N., Gilks W. Bayesian Inference Using Gibbs Sampling. Version 0.5 (Version ii) 1996, MRC Biostatistics Unit, Cambridge.
25. Spiegelhalter D., Thomas A., Best N., Lunn D. WinBUGS User Manual. Version 1.4 2003, MRC Biostatistics Unit, Cambridge, UK.
26. Vonesh E.F. A note on the use of laplace's approximation for nonlinear mixed-effects models. Biometrika 1996, 83(2):447-452.
27. Wang G. On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models. Ecological Modelling 2007, 200(3-4):521-528.
28. Wolfinger R., Xihong L. Two Taylor-series approximation methods for nonlinear mixed models. Computational Statistics & Data Analysis 1997, 25(4):465-490.
29. Zucchini W., MacDonald I. Hidden Markov Models for Time Series 2009, Chapman & Hall/CRC, London.