RT Journal Article
T1 Fitting DNA sequences through log-linear modelling with linear constraints
A1 Martín Apaolaza, Níriam
A1 Pardo Llorente, Leandro
AB For some discrete state series, such as DNA sequences, it can often be postulated that its probabilistic behaviour is given by a Markov chain. For making the decision on whether or not an uncharacterized piece of DNA is part of the coding region of a gene, under the Markovian assumption, there are two statistical tools that are essential to be considered: the hypothesis testing of the order in a Markov chain and the estimators of transition probabilities. In order to improve the traditional statistical procedures for both of them when stationarity assumption can be considered, a new version for understanding the homogeneity hypothesis is proposed so that log-linear modelling is applied for conditional independence jointly with homogeneity restrictions on the expected means of transition counts in the sequence. In addition we can consider a variety of test-statistics and estimators by using phi-divergence measures. As special case of them the well-known likelihood ratio test-statistics and maximum-likelihood estimators are obtained.
PB Taylor & Francis
SN 0233-1888
YR 2011
FD 2011
LK https://hdl.handle.net/20.500.14352/42424
UL https://hdl.handle.net/20.500.14352/42424
LA eng
DS Docta Complutense
RD 12 abr 2025