You are not currently logged in.
Access your personal account or get JSTOR access through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Fitting Markov Chain Models to Discrete State Series Such as DNA Sequences
Peter J. Avery and Daniel A. Henderson
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 48, No. 1 (1999), pp. 53-61
Stable URL: http://www.jstor.org/stable/2680818
Page Count: 9
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
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.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1999 Royal Statistical Society