D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution. Stat. Sci. 2 191-210 (1987).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....it is not surprising if these histories again di er from the presumed true evolutionary history of the organisms themselves. As an example, the modal inferred topology between humans, chimps and gorillas varies depending on the genes examined and evolutionary model imposed on the data (Barry and Hartigan, 1987; Suchard et al. submitted) To account for the uncertainty in topology, we propose using a dissimilarity measure that does not force a single topology assumption. This measure, d(x 1 ; x 2 ; xK ) is the distance between two or more scaled evolutionary histories de ned on the joint ....

Barry, D. and Hartigan, J. (1987). Statistical analysis of hominoid molecular evolution. Statistical Science, 2:191-210.

....In the case of the Markov chain on a tree, the parameters are the stochastic matrices and the topology of the tree. The maximization, which is to be performed numerically, turns out to become a difficult problem for large families of sequences, and some simplifications are often made (see however (Barry and Hartigan, 1987) for a general discussion of parameter rich models) In addition, the comparison of likelihoods for different tree topologies may be difficult (see the discussion in (Adachi and Hasegawa, 1992) The most common simplification amounts to assume that all the stochastic matrices associated to the ....

Barry, D. and Hartigan, J. (1987). Statistical analysis of hominoid molecular evolution. Stat. Sci., 2:191--210.

....1996) or if these histories again di er from the presumed true evolutionary history of the organisms themselves. As an example, the modal inferred topology between humans, chimps and gorillas varies depending on the genes examined and evolutionary model imposed on the data (Brown et al. 1982; Barry and Hartigan, 1987; Suchard et al. submitted) These problems are diminished using appropriate models and larger datasets. To account for the uncertainty in topology, we propose a measure of dissimilarity that does not force a single topology assumption. This measure, d(x 1 ; x 2 ; xK ) is the distance ....

Barry, D. and Hartigan, J. (1987), \Statistical analysis of hominoid molecular evolution," Statistical Science, 2, 191-210.

....surprising if these histories again di er from the presumed true evolutionary history of the organisms themselves. As an example, the modal inferred topology between humans, chimps and gorillas varies depending on the genes examined and evolutionary model imposed on the data (Brown et al. 1982; Barry and Hartigan, 1987; Suchard et al. submitted) To account for the uncertainity in topology, we propose using a measure of dissimilarity that does not force a single topology assumption. This measure, d(x 1 ; x 2 ; xK ) is the distance between two 8 or more scaled evolutionary histories de ned by a ....

Barry, D. and Hartigan, J. (1987), \Statistical analysis of hominoid molecular evolution," Statistical Science, 2, 191-210.

....and usually all of whose internal nodes have degree 3. Over the past few decades, many di#erent objective criteria and algorithms for reconstructing phylogenies have been developed, including (not exhaustively) parsimony [8, 11, 31] compatibility [25] distance [12, 30] and maximum likelihood [1, 8, 9]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to di#erent trees on the same set of species [24] It is thus of interest to compare phylogenies produced by di#erent methods or by the same method ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2(1987), 191-210.

....of n independent observations from the unknown distribution. The method of maximum likelihood estimates the true model by the Markov model that maximizes the probability of the observed data. Methods for computing maximum likelihood estimators in phylogenetic estimation are discussed in [18] and [19], for example. An estimator is said to be consistent if it is certain to converge to the true quantity as the sample size grows. More formally, given a parametrized family of distributions fP : 2 Thetag , let X 1 ; X 2 ; be independent and identically distributed observations from P ....

D. Barry and J. A. Hartigan. Statistical analysis of hominoid molecular evolution. Statistical Science, 2:191--210, (1987).

....is known, and usually has internal nodes of degree 3. Over the past few decades, many different objective criteria and algorithms for reconstructing phylogenies have been developed, including (not exhaustively) parsimony [6, 9, 28] compatibility [22] distance [10, 27] and maximum likelihood [6, 7, 2]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to different trees on the same set of species [18] It is thus of interest to compare phylogenies produced by different methods, or by the same ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2(1987), 191-210.

....and usually all of whose internal nodes have degree 3. Over the past few decades, many different objective criteria and algorithms for reconstructing phylogenies have been developed, including (not exhaustively) parsimony [8, 11, 31] compatibility [25] distance [12, 30] and maximum likelihood [1, 8, 9]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to different trees on the same set of species [24] It is thus of interest to compare phylogenies produced by different methods or by the same ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2(1987), 191-210.

....is known, and usually has internal nodes of degree 3. Over the past few decades, many different objective criteria and algorithms for reconstructing phylogenies have been developed, including (not exhaustively) parsimony [6, 9, 22] compatibility [17] distance [10, 21] and maximum likelihood [6, 7, 1]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to different trees on the same set of species [16] It is thus of interest to compare phylogenies produced by different methods, or by the same ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2(1987), 191-210.

....is known, and usually has internal nodes of degree 3. Over the past few decades, many different objective criteria and algorithms for reconstructing phylogenies have been developed, including (not exhaustively) parsimony [4, 7, 20] compatibility [15] distance [8, 19] and maximum likelihood [4, 5, 1]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to different trees on the same set of species [14] It is thus of interest to compare phylogenies produced by different methods, or by the same ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2(1987), 191-210.

....not persist, as both probabilities converge to 1 as n 1, but may be significant for any fixed n. 4. Maximum likelihood and an example One approach to phylogenetic inference is to model the evolutionary process so as to restrict P to a parametric subset of S d . Felsenstein (1981, 1983, 1992) Barry and Hartigan (1987), Golding and Felsenstein (1990) and Navidi et al. 1993) among others, have constructed parametric models and have advocated maximum likelihood estimation. Such models are based on branching Markov processes describing the evolution of each site from its ancestral state to its present ....

Barry, D. and Hartigan, J. A. (1987). Statistical analysis of hominoid molecular evolution. Statist. Sci. 2, 191--210.

....of species is one of the fundamental yet difficult problems in evolutionary genetics. Over the past few decades, many approaches for reconstructing evolutionary trees have been developed, including (not exhaustively) parsimony [12, 15, 39] compatibility [32] distance [16, 38] maximum likelihood [12, 13, 3]. The outcomes of these methods usually depend on the data and the amount of computational resources applied. As a result, in practice they often lead to different trees on the same set of species [28] It is thus of interest to compare evolutionary trees produced by different methods, or by the ....

D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution, Stat. Sci., 2, pp. 191-210, 1987.

....example of the measure of the discrepancy, which has been used in the literature [10, 23] is a weighted least square measure, i.e. of the form X 1i;jn w ij (D ij Gamma d ij ) 2 where D ij are the given distances and d ij are the distances computed from the tree. Maximum likelihood methods [18, 19, 8] relies on the statistical method of choosing a tree that maximizes the likelihood, i.e. maximizes the probability that the observed data would have occurred. Although this method is quite general and powerful, it is computationally intensive because of the complexity of the likelihood function. ....

D. Barry and J.A. Hartigan. Statistical analysis of hominoid molecular evolution, Stat. Sci., 2 (1987), pp. 191-210.

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Barry, D. and Hartigan, J. A. (1987b). Statistical analysis of hominoid molecular evolution. Statistical Science 2 191--210.

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D. Barry and J.A. Hartigan, Statistical analysis of hominoid molecular evolution. Stat. Sci. 2 191-210 (1987).

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D. Barry and JA Hartigan. Statistical analysis of hominoid molecular evolution. Statistical Science, (2):191--210, 1987.

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Barry, D. & Hartigan, J. (1987b). Statistical analysis of hominoid molecular evolution. Stat. Sci. 2, 191-210.

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