Testing the Order of Markov Dependence in DNA Sequences

M. L. Menéndez (), L. Pardo (), M. C. Pardo () and K. Zografos ()
Additional contact information
M. L. Menéndez: Polytechnical University of Madrid
L. Pardo: Complutense University of Madrid
M. C. Pardo: Complutense University of Madrid
K. Zografos: University of Ioannina

Methodology and Computing in Applied Probability, 2011, vol. 13, issue 1, 59-74

Abstract: Abstract DNA or protein sequences are usually modeled as probabilistic phenomena. The simplest model is created on the assumption that the nucleotides at the various sites are independently distributed. Usually the type of nucleotide at some site depends on the type at another site and therefore the DNA sequence is modeled as a Markov chain of random variables taking on the values A, G, C and T corresponding to the four nucleotides. First order or higher order Markov models provide better fit to a DNA sequence. Based on this remark, the aim of this paper is to present and study a family of test statistics for testing order Markov dependence in DNA sequences. This new family includes as a particular case the classical likelihood ratio test. A simulation study is presented in order to find test statistics, in this family, with a better behaviour than the likelihood ratio test.

Keywords: DNA sequence; Markov dependence; Likelihood ratio test; Phi-divergence test statistics; 92D20; 62M02 (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-008-9107-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:13:y:2011:i:1:d:10.1007_s11009-008-9107-1

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-008-9107-1

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().