Fitting Markov chain models to discrete state series such as DNA sequences

Discrete state series such as DNA sequences can often be modelled by Markov chains. The analysis of such series is discussed in the context of log-linear models. The data produce contingency tables with similar margins due to the dependence of the observations. However, despite the unusual structure...

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Bibliographic Details
Published in:Applied statistics 1999, Vol.48 (1), p.53-61
Main Authors: Avery, P. J., Henderson, D. A.
Format: Article
Language:English
Subjects:
Applications
Biology, psychology, social sciences
Contingency table
Degrees of freedom
DNA
Exact sciences and technology
Expected values
Introns
Log-linear model
Markov chain
Markov chains
Markovian processes
Mathematics
Modeling
Nucleotide sequences
Parametric models
Probability and statistics
Rainfall
Sciences and techniques of general use
Simulations
Statistical analysis
Statistical models
Statistics
Citations: Items that cite this one
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Summary:Discrete state series such as DNA sequences can often be modelled by Markov chains. The analysis of such series is discussed in the context of log-linear models. The data produce contingency tables with similar margins due to the dependence of the observations. However, despite the unusual structure of the tables, the analysis is equivalent to that for data from multinomial sampling. The reason why the standard number of degrees of freedom is correct is explained by using theoretical arguments and the asymptotic distribution of the deviance is verified empirically. Problems involved with fitting high order Markov chain models, such as reduced power and computational expense, are also discussed.
ISSN:0035-9254
1467-9876
DOI:10.1111/1467-9876.00139