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oai:docta.ucm.es:20.500.14352/424242023-08-25T12:58:04Zcom_20.500.14352_14col_20.500.14352_15

Fitting DNA sequences through log-linear modelling with linear constraints Martín Apaolaza, Níriam Pardo Llorente, Leandro 519.234 contingency table log-linear model restricted estimator conditional test statistic Maximum-likelihood methods Estadística matemática (Matemáticas) 1209 Estadística 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. Depto. de Estadística e Investigación Operativa Fac. de Ciencias Matemáticas TRUE pub 2023-06-20T00:20:31Z 2023-06-20T00:20:31Z 2011 journal article https://hdl.handle.net/20.500.14352/42424 0233-1888 10.1080/02331888.2010.485275 eng MTM2009-10072 BSCH-UCM2008-910707 restricted access application/pdf Taylor & Francis