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Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm

Author

Listed:
  • Hobolth Asger

    (Bioinformatics Research Center, University of Aarhus, Denmark)

  • Jensen Jens Ledet

    (Department of Theoretical Statistics, University of Aarhus, Denmark)

Abstract

We describe statistical inference in continuous time Markov processes of DNA sequences related by a phylogenetic tree. The maximum likelihood estimator can be found by the expectation maximization (EM) algorithm and an expression for the information matrix is also derived. We provide explicit analytical solutions for the EM algorithm and information matrix.

Suggested Citation

  • Hobolth Asger & Jensen Jens Ledet, 2005. "Statistical Inference in Evolutionary Models of DNA Sequences via the EM Algorithm," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-22, August.
    • Handle: RePEc:bpj:sagmbi:v:4:y:2005:i:1:n:18
    DOI: 10.2202/1544-6115.1127
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    Cited by:

    1. Hobolth, Asger & Siren, Jukka, 2016. "The multivariate Wright–Fisher process with mutation: Moment-based analysis and inference using a hierarchical Beta model," Theoretical Population Biology, Elsevier, vol. 108(C), pages 36-50.
    2. Alexander Kremer & Rafael Weißbach, 2013. "Consistent estimation for discretely observed Markov jump processes with an absorbing state," Statistical Papers, Springer, vol. 54(4), pages 993-1007, November.
    3. Nils Lid Hjort & Cristiano Varin, 2008. "ML, PL, QL in Markov Chain Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(1), pages 64-82, March.
    4. Zheng Wei & Zhao Hongyu, 2013. "Studying the evolution of transcription factor binding events using multi-species ChIP-Seq data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 1-15, March.

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