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Covariance estimates for distances computed from IPAs in the context of specific parametric substitution models have been reported by Hasegawa et al. [ 9] and Bulmer [ 6], and were generalized by Susko [ 10] to all Markovian models of evolution.
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A particular strength of the phylo-grammar framework is the ease with which it is (theoretically) possible to refine the models, adding new components to better model target features [21] or altering the parametric structure of the substitution rate matrices, a common practice when training data are sparse [22] [24].
Simple substitution methods, parametric methods based on survival techniques, and nonparametric methods, such as the Wilcoxon rank-sum test, can be used for group comparisons.
For example, simple substitution methods, parametric methods, nonparametric Kaplan-Meier methods, and robust regression on order statistics methods can be used to obtain summary statistics (e.g., means, standard deviations, medians, and percentiles) for left-censored data.
These models were used for tree building, substitution mapping and parametric bootstrap simulations.
The semiparametric approach combines a parametric model with a different substitution rate on every branch with a nonparametric roughness penalty which penalizes the model if rates change too quickly from branch to branch.
PHYML [ 36] was used to build maximum likelihood trees, assuming a JTT model of substitution and non-parametric bootstrapping (100 replicates).
Here, we present SOCS-B, a reference-based, un-gapped alignment algorithm for the SOLiD System that is tolerant of both bisulfite-induced nucleotide substitutions and a parametric number of sequencing errors, facilitating bisulfite sequencing on this platform.
Three different approaches were considered to handle the left censored data: (1) a substitution method, (2) a parametric method using the maximum likelihood estimation (MLE) and (3) a non-parametric method using the Kaplan Meier (KM) estimator.
The external loop is designed to move the translational dynamics of the robot to follow smooth parametric functions using an appropriate variables substitution.
A new ML tree was constructed using the WAG model of amino acid substitution, followed by the parametric approximate likelihood ratio test (aLRT) to assess the significance of individual branches.
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