Non-linear state space modelling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 49, Issue 3, 2000, Pages 327-342

  Millar, Russell B ^a   Meyer, Renate ^b  

^a UNIVERSITY OF AUCKLAND   (New Zealand)

^b NONE

Author keywords

Bayesian analysis; Fish stock assessment; Markov chain Monte Carlo sampling; Nonlinear state space models; Surplus production models; Tuna

Indexed keywords

EID: 0034360571     PISSN: 00359254     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9876.00195     Document Type: Article

References (45)

1. Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Belmont: Wadsworth and Brooks/Cole.
2. Best, N. G., Cowles, M. K. and Vines, S. K. (1995) CODA Manual Version 0.30. Cambridge: Medical Research Council Biostatistics Unit.
3. Breen, P. A. (1991) Assessment of the red rock lobster (Jasus edwardsii) North and South Island stock, November 1991. Assessment Research Document 91/16. New Zealand Ministry of Fisheries, Wellington.
4. Carlin, B. P., Polson, N. G. and Stoffer, D. S. (1992) A Monte Carlo approach to nonnormal and nonlinear state-space modelling. J. Am. Statist. Ass., 87, 493-500.
5. Freeman, S. N. and Kirkwood, G. P. (1995) On a structural time series method for estimating stock biomass and recruitment from catch and effort data. Fish. Res., 22, 77-98.
6. Gelfand, A. E. and Smith, A. F. M. (1990) Sampling-based approaches to calculating marginal densities. J. Am. Statist. Ass., 85, 398-409.
7. Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995) Bayesian Data Analysis. London: Chapman and Hall.
8. Gelman, A., Meng, X. and Stern, H. (1996) Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statist. Sin., 6, 733-807.
9. Gilks, W. R. (1996) Full conditional distributions. In Markov Chain Monte Carlo in Practice (eds W. R. Gilks, S. Richardson and D. J. Spiegelhalter), pp. 75-88. London: Chapman and Hall.
10. Gilks, W. R., Best, N. G. and Chan, K. K. C. (1995) Adaptive rejection Metropolis sampling within Gibbs sampling. Appl. Statist., 44, 455-472.
11. Gilks, W. R., Neal, R. M., Best, N. G. and Tan, K. K. C. (1997) Adaptive rejection Metropolis sampling. Appl. Statist., 46, 541-542.
12. Gilks, W. R., Richardson, S. and Spiegelhalter, D. J. (eds) (1996) Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
13. Gilks, W. R. and Roberts, G. O. (1996) Strategies for improving MCMC. In Markov Chain Monte Carlo in Practice (eds W. R. Gilks, S. Richardson and D. J. Spiegelhalter), pp. 89-114. London: Chapman and Hall.
14. Gudmundsson, G. (1994) Time series analysis of catch-at-age observations. Appl. Statist., 43, 117-126.
15. -(1995) Time series analysis of catch-at-length observations. ICES J. Mar. Sci., 52, 781-795.
16. Heidelberger, P. and Welch, P. (1983) Simulation run length control in the presence of an initial transient. Oper. Res., 31, 1109-1144.
17. Hilborn, R. (1992) Current and future trends in fisheries stock assessment and management. S. Afr. J. Mar. Sci., 12, 975-988.
18. -(1997) Statistical hypothesis testing and decision theory in fisheries science. Fisheries, 22, 19-20.
19. Hilborn, R. and Liermann, M. (1998) Standing on the shoulders of giants: learning from experience in fisheries. Rev. Fish. Biol. Fish., 8, 273-283.
20. Hilborn, R., Pikitch, E. K. and McAllister, M. K. (1994) A Bayesian estimation and decision analysis for an age-structured model using biomass survey data. Fish. Res., 19, 17-30.
21. Hilborn, R. and Walters, C. J. (1992) Quantitative Fisheries Stock Assessment: Choice, Dynamics and Uncertainty. New York: Chapman and Hall.
22. Kalman, R. E. (1960) A new approach to linear filtering and prediction problems. J. Basic Engng, 82, 34-45.
23. Kass, R. E. and Wasserman, L. (1996) The selection of prior distributions by formal rules. J. Am. Statist, Ass., 91, 1343-1370.
24. Kimura, D. K., Balsiger, J. W. and Ito, D. H. (1996) Kalman filtering the delay difference equation: practical approaches and simulations. Fish. Bull., 94, 678-691.
25. Kinas, P. G. (1996) Bayesian fishery stock assessment and decision making using adaptive importance sampling. Can. J. Fish. Aquat. Sci., 53, 414-423.
26. Liermann, M. and Hilborn, R. (1997) Depensation in fish stocks: a hierarchic Bayesian meta-analysis. Can. J. Fish. Aquat. Sci., 54, 1976-1984.
27. Lin, X. and Breslow, N. E. (1996) Bias correction in generalized linear mixed models with multiple components of dispersion. J. Am. Statist. Ass., 91, 1007-1016.
28. Ludwig, D., Walters, C. J. and Cooke, J. (1988) Comparison of two models and two estimation methods for catch and effort data. Nat. Resour. Modlng, 2, 457-498.
29. McAllister, M. K., Pikitch, E. K., Punt, A. E. and Hilborn, R. (1994) A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can. J. Fish. Aquat. Sci., 51, 2673-2687.
30. Myers, R. A., Bowen, K. G. and Barrowman, N. J. (1999) The maximum reproductive rate of fish at low population sizes. Can. J. Fish. Aquat. Sci., to be published.
31. Myers, R. A., Mertz, G. and Fowlow, P. S. (1997) Maximum population growth rates and recovery times for Atlantic cod, Gadus morhua. Fish. Bull, 95, 762-772.
32. Pella, J. J. (1993) Utility of structural time series models and the Kalman filter for predicting consequences of fishery actions. In Proc. Int. Symp. Management Strategies for Exploited Fish Populations (eds G. Kruse, D. M. Eggers, R. J. Marasco, C. Pautzke and T. J. Quinn II), pp. 571-593. Fairbanks: University of Alaska.
33. Polacheck, T., Hilborn, R. and Punt, A. E. (1993) Fitting surplus production models: comparing methods and measuring uncertainty. Can. J. Fish. Aquat. Sci., 50, 2597-2607.
34. Prager, M. H. (1994) A suite of extensions to a nonequilibrium surplus-production model. Fish. Bull., 92, 374-389.
35. Punt, A. E., Butterworth, D. S. and Penney, A. J. (1995) Stock assessment and risk analysis for the South Atlantic population of albacore Thunnus alalunga using an age-structured production model. S. Afr. J. Mar. Sci., 16, 287-310.
36. Punt, A. E. and Hilborn, R. (1997) Fisheries stock assessment and decision analysis: the Bayesian approach. Rev. Fish Biol. Fish., 7, 35-63.
37. Reed, W. J. and Simons, C. M. (1996) Analyzing catch-effort data by means of the Kalman filter. Can. J. Fish. Aquat. Sci., 53, 2157-2166.
38. Richards, L. J. and Schnute, J. T. (1998) Model complexity and catch-age analysis. Can. J. Fish. Aquat. Sci., 55, 949-957.
39. Ricker, W. E. (1975) Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Bd Can., 191.
40. Schaefer, M. B. (1954) Some aspects of the dynamics of populations important to the management of the commercial marine fisheries. Bull. Inter-Am. Trop. Tuna Commsn, 1, 25-56.
41. Schnute, J. T. (1994) A general framework for developing sequential fisheries models. Can. J. Fish. Aquat. Sci., 51, 1676-1688.
42. Spiegelhalter, D. J., Freedman, L. S. and Parmar, M. K. B. (1994) Bayesian approaches to randomized trials (with discussion). J. R. Statist. Soc. A, 157, 357-416.
43. Sullivan, P. J. (1992) A Kalman filter approach to catch-at-length analysis. Biometrics, 48, 237-257.
44. Walters, C. and Ludwig, D. (1994) Calculation of Bayes posterior probability distribution for key population parameters. Can. J. Fish. Aquat. Sci., 51, 713-722.
45. West, M. (1993) Approximating posterior distributions by mixtures. J. R. Statist. Soc. B, 55, 409-422.