Model weights and the foundations of multimodel inference

Ecology

Volumn 87, Issue 10, 2006, Pages 2626-2635

  Link, William A ^a   Barker, Richard J ^b  

^a PATUXENT WILDLIFE RESEARCH CENTER   (United States)

^b UNIVERSITY OF OTAGO   (New Zealand)

Author keywords

AIC; Akaike's information criterion; Bayes factors; Bayesian inference; Bayesian information criterion; BIC; Model averaging; Model selection; Salmo trutta

Indexed keywords

AKAIKE INFORMATION CRITERION; BAYESIAN ANALYSIS; ECOLOGICAL MODELING; LOGISTICS; REGRESSION ANALYSIS;

ANIMAL; ARTICLE; BAYES THEOREM; BIOLOGICAL MODEL; ECOLOGY; METHODOLOGY; TROUT;

ANIMALS; BAYES THEOREM; ECOLOGY; MODELS, BIOLOGICAL; TROUT;

SALMO TRUTTA;

EID: 33845360384     PISSN: 00129658     EISSN: 00129658     Source Type: Journal    
DOI: 10.1890/0012-9658(2006)87[2626:MWATFO]2.0.CO;2     Document Type: Article

References (22)

1. Anderson, D. R., W. A. Link, D. H. Johnson, and K. P. Burnham. 2001. Suggestions for presenting the results of data analysis. Journal of Wildlife Management 65:373-378.
2. Berger, J. O., and L. R. Pericchi. 1996. The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association 91:109-122.
3. Berger, J. O., and L. R. Pericchi. 1998. On criticisms and comparisons of default Bayes factors for model selection and hypothesis testing. ISDS Discussion Paper 97-43. Duke University, Durham, North Carolina, USA.
4. Box, G. E. P. 1976. Science and statistics. Journal of the American Statistical Association 71:791-799.
5. Burnham, K. P., and D. R. Anderson. 1998. Model selection and inference: a practical information-theoretic approach. Springer-Verlag, New York, New York, USA.
6. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York, New York, USA.
7. Burnham, K. P., and D. R. Anderson. 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33:261-304.
8. Carlin, B. P., and S. Chib. 1995. Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society (London), Series B 57:473-484.
9. Chatfield, C. 1995. Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society (London), Series A 158:419-466.
10. Claeskens, G., and N. L. Hjort. 2003. The focused information criterion. Journal of the American Statistical Association 98:900-916.
11. Draper, D. 1995. Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society (London), Series B 57:45-97.
12. Green, P. J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82:711-732.
13. Guthery, F. S., L. A. Brennan, M. J. Peterson, and J. J. Lusk. 2005. Information theory in wildlife science: critique and viewpoint. Journal of Wildlife Management 69:457-465.
14. Hjort, N. L., and G. Claeskens. 2003. Frequentist model average estimators. Journal of the American Statistical Association, 98:879-899.
15. Hoeting, J. A., D. Madigan, A. E. Rafterty, and C. T. Volinsky. 1999. Bayesian model averaging: a tutorial. Statistical Science 14:382-417.
16. Kadane, J. B., and N. A. Lazar. 2004. Methods and criteria for model selection. Journal of the American Statistical Association 99:279-290.
17. Kass, R. E., and A. E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association 90:773-795.
18. Raftery, A. E., and Y. Zheng. 2003. Discussion: inference and interpretability considerations in Frequentist model averaging and selection. Journal of the American Statistical Association 98:931-937.
19. Richards, S. A. 2005. Testing ecological theory using the information-theoretic approach: examples and cautionary results. Ecology 86:2805-2814.
20. Spiegelhalter, D., A. Thomas, and N. G. Best. 2000. Win-BUGS, version 1.3 user manual. MRC Biostatistics Unit, Cambridge, UK.
21. Stephens, P. A., S. W. Buskirk, and G. D. Hayward. and C. Martínez del Rio. 2005. Information theory and hypothesis testing: a call for pluralism. Journal of Applied Ecology 42:4-12.
22. Wintle, B. A., M. A. McCarthy, C. T. Volinsky, and R. P. Kavanagh. 2003. The use of Bayesian model averaging to better represent uncertainty in ecological models. Conservation Biology 17:1579-1590.