General strategies for the analysis of ringing data

Bird Study

Volumn 46, Issue SUPPL., 1999, Pages

  Anderson, David R ^a   Burnham, Kenneth P ^a  

^a Immunology and Pathology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RINGING; STATISTICAL ANALYSIS;

EID: 0033405656     PISSN: 00063657     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (47)

1. Brownie, C., Anderson, D.R., Burnham, K.P. & Robson, D.S. (1985) Statistical Inference from Band Recovery Data Handbook, 2nd edn. US Fish and Wildlife Services Resource Publication 156.
2. Lebreton, J-D., Burnham, K.P., Clobert, J. & Anderson, D.R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case studies. Ecol. Monogr., 62, 67-118.
3. Burnham, K.P., Anderson, D.R., White, G.C., Brownie, C. & Pollock, K.H. (1987) Design and Analysis Methods for Fish Survival Experiments Based on Release-Recapture. American Fisheries Society Monograph 5.
4. Chatfield, C. (1995) Model uncertainty, data mining and statistical inference. J. R. Stat. Soc. A, 158, 419-466.
5. Freedman, D.A. (1983) A note on screening regression equations. Am. Stat., 37, 152-155.
6. Burnham, K.P., Anderson, D.R. & White, G.C. (1996) Meta-analysis of vital rates of the Northern Spotted Owl. Stud. Avion Biol., 17, 92-101.
7. Anderson, D.R. & Burnham, K.P. (1999) Understanding information criteria for selection among capture-recapture or ring recovery models. Bird Study, 46 (suppl.), 14-21.
8. Peirce, C.S. (1955) Abduction and induction. In Philosophical Writings of Peirce (ed. J. Buchler), pp. 150-156. Dover, New York.
9. Shibata, R. (1989) Statistical aspects of model selection. In From Data to Model (ed. J.C. Willems), pp. 215-240. Springer-Verlag, London.
10. Kuliback, S. & Leibler, R.A. (1951) On information and sufficiency. Ann. Math. Stat., 22, 79-86.
11. Akaike, H. (1973) Information theory as an extension of the maximum likelihood principle. In Second International Symposium on Information Theory (eds. B.N. Petrov & F. Csaki), pp. 267-281. Akademiai Kiado, Budapest.
12. Akaike, H. (1974) A new look at the statistical model identification. IEEE Trans. Automatic Control, 19, 716-723.
13. Akaike, H. (1977) On entropy maximization principle. In Applications of Statistics (ed. P.R. Krishnaiah), pp. 27-41. North-Holland, Amsterdam.
14. Akaike, H. (1981) Likelihood of a model and information criteria. J. Econometrics, 16, 3-14.
15. Akaike, H. (1981) Modern development of statistical methods. In Trends and Progress in System Identification (ed. P. Eykhoff), pp. 169-184. Pergamon Press, Paris.
16. Akaike, H. (1983) Statistical inference and measurement of entropy. In Scientific Inference, Data Analysis, and Robustness (eds G.E.P. Box, T. Leonard & C-F. Wu), pp. 165-189. Academic Press, London.
17. Akaike, H. (1985) Prediction and entropy. In A Celebration of Statistics (eds A.C. Atkinson & S.E. Fienberg), pp. 1-24. Springer-Verlag, New York.
18. Akaike, H. (1992) Information theory and an extension of the maximum likelihood principle. In Breakthroughs in Statistics (eds S. Kotz & N.L. Johnson), Vol. 1, pp. 610-624. Springer-Verlag, London.
19. Akaike, H. (1994) Implications of the informational point of view on the development of statistical science. In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Engineering and Scientific Applications (ed. H. Bozdogan), Vol. 3, pp. 27-38.. Kluwer, Dordrecht.
20. Bozdogan, H. (1987) Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52, 345-370.
21. deLeeuw, J. (1992) Introduction to Akaike (1973): Information theory and an extension of the maximum likelihood principle. In Breakthroughs in Statistics, Vol. 1 (eds S. Kotz & N.L. Johnson), pp. 599-609. Springer-Verlag, London.
22. Sugiura, N. (1978) Further analysis of the data by Akaike's information criterion and the finite corrections. Commun. Stat. Theory Methods, A7, 13-26.
23. Hurvich, C.M. & Tsai, C-L. (1989) Regression and time series model selection in small samples. Biometrika, 76, 297-307.
24. Hurvich, C.M. & Tsai, C-L. (1995) Model selection for extended quasi-likelihood models in small samples. Biometrics, 51, 1077-1084.
25. Wedderburn, R.W. M. (1974) Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika, 61, 439-447.
26. Anderson, D.R., Burnham, K.P. & White, G.C. (1994) AIC model selection in overdispersed capture-recapture data. Ecology, 75, 1780-1793.
27. Stone, M. (1977) An asymptotic equivalence of choice of model by cross validation and Akaike's criterion. J. R. Stat. Soc. B, 39, 44-47.
28. Takeuchi, K. (1976) Distribution of informational statistics and a criterion of model fitting. SuriKagaku (Mathematic Sciences), 153, 12-18. (in Japanese)
29. Shibata, R. (1998) Statistical Model Selection. Springer-Verlag, New York.
30. Burnham, K.P., Anderson, D.R. & White, G.C. (1994) Evaluation of the Kullback-Leibler discrepancy for model selection in open population capture-recapture models. Biometrical J., 36, 299-315.
31. Burnham, K.P., White, G.C. & Anderson, D.R. (1995) Model selection in the analysis of capture-recapture data. Biometrics, 51, 888-898.
32. Burnham, K.P., Anderson, D.R. & White, G.C. (1995) Selection among open populations capture-recapture models when capture probabilities are heterogeneous. J. Appl. Stat., 22, 611-624.
33. Anderson, D.R., Burnham, K.P. & White, G.C. (1998) Comparison of Akaike information criterion and consistent Akaike information criterion for model selection and statistical inference from capture-recapture studies. J. Appl. Stat., 25, 263-282.
34. Linhart, H. & Zucchini, W. (1986) Model Selection. Wiley, New York.
35. Stone, M. & Brooks, R.J. (1990) Continuum regression: cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principle components regression (with discussion). J. R. Stat. Soc. B, 52, 237-269.
36. Shibata, R. (1983) A theoretical view of the use of AIC. In Time Series Analysis: Theory and Practice (ed. O.D. Anderson), pp. 237-244. Elsevier North-Holland, Amsterdam.
37. Shibata, R. (1997) Bootstrap estimate of Kullback-Leibler information for model selection. Stat. Sinica, 7, 375-394.
38. Buckland, S.T., Burnham, K.P. & Augustin, N.H. (1997) Model selection: an integral part of inference. Biometrics, 53, 603-618.
39. White, G.C. & Burnham, K.P. (1999) Program MARK: survival rate estimation from both live and dead encounters. Bird Study, 46 (suppl.), 120-139.
40. Brownie, C, Hines, J.E., Nichols, J.D., Pollock, K.H. & Hestbeck, J.B. (1993) Capture-recapture studies for multiple strata including non-Markovian transitions. Biometrics, 49, 1173-1187.
41. Nichols, J.D., Brownie, C., Hines, J.E., Pollock, K.H. & Hestbeck, J.B. (1993) The estimation of exchanges among populations or subpopulations. In Marked Individuals in the Study of Bird Populations (eds J.-D. Lebreton & P. M. North), pp. 265-280. Birkhauser-Verlag, Boston.
42. Yoccoz, N.G. (1991) Use, overuse, and misuse of significance tests in evolutionary biology and ecology. Bull. Ecol. Soc. Am., 72, 106-111.
43. Bozdogan, H. (1994) Editor's general preface. In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Engineering and Scientific Applications (ed. H. Bozdogan), Vol. 3, pp. ix-xii.. Kluwer, Dordrecht.
44. Johnson, D.K. (1995) Statistical sirens: the allure of nonparametrics. Ecology, 76, 1998-2000.
45. Stewart-Oaten, A. (1995) Rules and judgments in statistics: three examples. Ecology, 76, 2001-2009.
46. Nester, M.R. (1996) An applied statistician's creed. Appl. Stat., 45, 401-410.
47. Graybill, F.A. & Iyer, H.K. (1994) Regression Analysis: Concepts and Applications. Duxbury Press, Belmont, CA.