Bayesian methods for hierarchical models: Are ecologists making a Faustian bargain?

Ecological Applications

Volumn 19, Issue 3, 2009, Pages 581-584

  Lele, Subhash R ^a   Dennis, Brian ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF IDAHO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; ECOSYSTEM MODELING; HIERARCHICAL SYSTEM;

ALGORITHM; ARTICLE; BAYES THEOREM; ECOLOGY; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; ECOLOGY; MODELS, STATISTICAL; RESEARCH DESIGN;

EID: 63849096685     PISSN: 10510761     EISSN: None     Source Type: Journal    
DOI: 10.1890/08-0549.1     Document Type: Article

References (16)

1. Barnett, V. 1999. Comparative statistical inference. John Wiley, New York, New York, USA.
2. Chang, K. 2008. Nobel winner retracts research paper. New York Times, 10 March 2008.
3. Cox, D. R. 2005. Frequentist and Bayesian statistics: a critique. In L. Louis and M. K. Unel, editors. Proceedings of the Statistical Problems in Particle Physics, Astrophysics and Cosmology. Imperial College Press, UK. (http://www. physics.ox.ac.uk/phystat05/proceedings/default.htm)
4. Cressie, N. A. C, C. A. Calder, J. S. Clark, J. M. Ver Hoef, and C. K. Wikle. 2009. Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications 19:553-570.
5. Dawid, A. P., M. Stone, and J. V. Zidek. 1973. Marginalization paradoxes in Bayesian and structural inference. Journal of the Royal Statistical Society B 35:189-233.
6. Dennis, B. 2004. Statistics and the scientific method in ecology. Pages 327-378 in M. L. Taper and S. R. Lele, editors. The nature of scientific evidence: statistical, empirical and philosophical considerations. University of Chicago Press, Chicago, Illinois, USA.
7. De Valpine, P. 2004. Monte Carlo state-space likelihoods by weighted kernel density estimation. Journal of the American Statistical Association 99:523-536.
8. Heinrich, J. 2005. The Bayesian approach to setting limits: what to avoid. In L. Louis and M. K. Unel, editors. Proceedings of the Statistical Problems in Particle Physics, Astrophysics and Cosmology. Imperial College Press, UK. (http://www. physics.ox.ac.uk/phystat05/proceedings/default. htm)
9. Laird, N. M., and T. A. Louis. 1987. Empirical Bayes confidence intervals based on bootstrap samples. Journal of the American Statistical Association 82:739-750.
10. Lele, S. R. 2004. Elicit data, not prior: on using expert opinion in ecological studies. Pages 410-436 in M. L. Taper and S. R. Lele, editors. The nature of scientific evidence: statistical, empirical and philosophical considerations. University of Chicago Press, Chicago, Illinois, USA.
11. Lele, S. R., and K. L. Allen. 2006. On using expert opinion in ecological analyses: a frequentist approach. Environmetrics 17:683-704.
12. Lele, S. R., and A. Das. 2000. Elicited data and incorporation of expert opinion for statistical inference in spatial studies. Mathematical Geology 32:465-487.
13. Lele, S. R., B. Dennis, and F. Lutscher. 2007. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecology Letters 10:551-563.
14. Mitchell, A. F. S. 1967. Discussion of paper by I. J. Good. Journal of the Royal Statistical Society, Series B 29:423-124. Press, S. J. 2003. Subjective and objective Bayesian statistics. Second edition. John Wiley, New York, New York, USA.
15. Royall, R. 2000. On the probability of observing misleading statistical evidence. Journal of the American Statistical Association 95:760-768.
16. Tyul, F., R. Gerlach, and K. Mengersen. 2008 A comparison of Bayes-Laplace, Jeffreys, and other priors: the case of zero events. American Statistician 62:40-14.