1. Yang, Z., Goldman, N., and Friday, A. (1995) Maximum likelihood trees from DNA sequences: a peculiar statistical estimation problem.Syst Biol44, 384–99.

   Google Scholar 

2. Tamura, K. (1994) Model selection in the estimation of the number of nucleotide substitutions.Mol Biol Evol11, 154–57.

   PubMed CAS  Google Scholar 

3. Zhang, J. (1999) Performance of likelihood ratio tests of evolutionary hypotheses under inadequate substitution models.Mol Biol Evol16, 868–75.

   PubMed CAS  Google Scholar 

4. Lemmon, A. R., and Moriarty, E. C. (2004) The importance of proper model assumption in Bayesian phylogenetics.Syst Biol53, 265–77.

   Article PubMed  Google Scholar 

5. Buckley, T. R., and Cunningham, C. W. (2002) The effects of nucleotide substitution model assumptions on estimates of nonparametric bootstrap support.Mol Biol Evol19, 394–405.

   Article PubMed CAS  Google Scholar 

6. Sullivan, J., and Swofford, D. L. (1997) Are guinea pigs rodents? The importance of adequate models in molecular phylogenies.J Mamm Evol4, 77–86.

   Article  Google Scholar 

7. Kelsey, C. R., Crandall, K. A., and Voevodin, A. F. (1999) Different models, different trees: the geographic origin of PTLV-I.Mol Phylogenet Evol13, 336–47.

   Article PubMed CAS  Google Scholar 

8. Pupko, T., Huchon, D., Cao, Y., Okada, N., and Hasegawa, M. (2002) Combining multiple data sets in a likelihood analysis: which models are the best?Mol Biol Evol19, 2294–307.

   Article PubMed CAS  Google Scholar 

9. Posada, D., and Buckley, T. R. (2004) Model selection and model averaging in phylogenetics: advantages of Akaike Information Criterion and Bayesian approaches over likelihood ratio tests.Syst Biol53, 793–808.

   Article PubMed  Google Scholar 

10. Sullivan, J., and Joyce, P. (2005) Model selection in phylogenetics.Annu Rev Ecol Evol. Syst.36, 445–66.

    Article  Google Scholar 

11. Alfaro, M. E., and Huelsenbeck, J. P. (2006) Comparative performance of Bayesian and AIC-based measures of phylogenetic model uncertainty.Syst Biol55, 89–96.

    Article PubMed  Google Scholar 

12. Ripplinger, J., and Sullivan, J. (2008) Does choice in model selection affect maximum likelihood analysis?Syst Biol57, 76–85.

    Article PubMed  Google Scholar 

13. Goldman, N. (1993) Statistical tests of models of DNA substitution.J Mol. Evol36, 182–98.

    Article PubMed CAS  Google Scholar 

14. Kendall, M., and Stuart, A. (1979) The Advanced Theory of Statistics, Charles Griffin, London.

    Google Scholar 

15. Posada, D., and Crandall, K. A. (2001) Selecting the best-fit model of nucleotide substitution.Syst Biol50, 580–601.

    Article PubMed CAS  Google Scholar 

16. Akaike, H. (1974) A new look at the statistical model identification.IEEE Trans. Aut. Control19, 716–23.

    Article  Google Scholar 

17. Kullback, S., and Leibler, R. A. (1951) On information and sufficiency.Ann Math Stat22, 79–86.

    Article  Google Scholar 

18. Sugiura, N. (1978) Further analysis of the data by Akaike's information criterion and the finite corrections.Comm Statist Theor MethA7, 13–26.

    Article  Google Scholar 

19. Hurvich, C. M., and Tsai, C.-L. (1989) Regression and time series model selection in small samples.Biometrika76, 297–307.

    Article  Google Scholar 

20. Schwarz, G. (1978) Estimating the dimension of a model.Ann Stat6, 461–64.

    Article  Google Scholar 

21. Minin, V., Abdo, Z., Joyce, P., and Sullivan, J. (2003) Performance-based selection of likelihood models for phylogeny estimation.Syst Biol52, 674–83.

    Article PubMed  Google Scholar 

22. Abdo, Z., Minin, V. N., Joyce, P., and Sullivan, J. (2005) Accounting for uncertainty in the tree topology has little effect on the decision-theoretic approach to model selection in phylogeny estimation.Mol Biol Evol22, 691–703.

    Article PubMed CAS  Google Scholar 

23. Burnham, K. P., and Anderson, D. R. (1998)Model Selection and Inference: A Practical Information-Theoretic Approach, Springer-Verlag, New York, NY.

    Google Scholar 

24. Burnham, K. P., and Anderson, D. R. (2003)Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer-Verlag, New York, NY.

    Google Scholar 

25. Posada, D. (2003)Current Protocols in Bioinformatics (Baxevanis, A. D., Davison, D. B., Page, R. D. M., Petsko, G. A., Stein, L. D., and Stormo, G. D., Eds.), pp. 6.5.1–6.5.14, John Wiley & Sons, Inc., New York

    Google Scholar 

26. Madigan, D. M., and Raftery, A. E. (1994) Model selection and accounting for model uncertainty in graphical models using Occam's Window.J Amer Stat Assoc89, 1335–46.

    Article  Google Scholar 

27. Wasserman, L. (2000) Bayesian model selection and model averaging.J Math Psychol44, 92–107.

    Article PubMed  Google Scholar 

28. Hoeting, J. A., Madigan, D., and Raftery, A. E. (1999) Bayesian model averaging: a tutorial.Stat Sci14, 382–417.

    Article  Google Scholar 

29. Raftery, A. E. (1996)Markov chain Monte Carlo in Practice (Gilks, W. R., Richardson, S., and Spiegelhalter, D. J., Eds.), pp. 163–87, Chapman & Hall, London, New York.

    Google Scholar 

30. Gilbert, D. (2007) ReadSeq, Indiana University, Bloomington.

    Google Scholar 

31. Guindon, S., and Gascuel, O. (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood.Syst Biol52, 696–704.

    Article PubMed  Google Scholar 

32. Felsenstein, J. (2005) Phylip, Department of Genome Sciences, University of Washington, Seattle.

    Google Scholar 

33. Gascuel, O. (1997) BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data.Mol Biol Evol14, 685–95.

    PubMed CAS  Google Scholar 

34. Swofford, D. L. (2000) PAUP*, Sinauer Associates, Sunderland, Massachusetts.

    Google Scholar 

35. Bollback, J. P. (2002) Bayesian model adequacy and choice in phylogenetics.Mol Biol Evol19, 1171–80.

    Article PubMed CAS  Google Scholar 

36. Ohta, T. (1992) Theoretical study of near neutrality. II. Effect of subdivided population structure with local extinction and recolonization.Genetics130, 917–23.

    Google Scholar 

37. Goldman, N., and Whelan, S. (2000) Statistical tests of gamma-distributed rate heterogeneity in models of sequence evolution in phylogenetics.Mol Biol Evol17, 975–78.

    PubMed CAS  Google Scholar 

38. Huelsenbeck, J. P., Larget, B., and Alfaro, M. E. (2004) Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo.Mol Biol Evol21, 1123–33.

    Article PubMed CAS  Google Scholar 

39. Jukes, T. H., and Cantor, C. R. (1969)Mammalian Protein Metabolism (Munro, H. M., Ed.), pp. 21–132, Academic Press, New York, NY.

    Google Scholar 

40. Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach.J Mol Evol17, 368–76.

    Article PubMed CAS  Google Scholar 

41. Kimura, M. (1980) A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences.J Mol Evol16, 111–20.

    Article PubMed CAS  Google Scholar 

42. Hasegawa, M., Kishino, K., and Yano, T. (1985) Dating the human-ape splitting by a molecular clock of mitochondrial DNA.J Mol Evol22, 160–74.

    Article PubMed CAS  Google Scholar 

43. Tamura, K., and Nei, M. (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees.Mol Biol Evol10, 512–26.

    PubMed CAS  Google Scholar 

44. Kimura, M. (1981) Estimation of evolutionary distances between homologous nucleotide sequences.Proc Natl Acad Sci USA78, 454–58.

    Article PubMed CAS  Google Scholar 

45. Zharkikh, A. (1994) Estimation of evolutionary distances between nucleotide sequences.J Mol Evol39, 315–29.

    Article PubMed CAS  Google Scholar 

46. Tavaré, S. (1986)Some Mathematical Questions in Biology – DNA Sequence Analysis (Miura, R. M., Ed.), Vol. 17, pp. 57–86, American Mathematical Society, Providence, RI.

    Google Scholar 

Download references