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A few publications have begun to address the issue of summary statistic selection [ 18– 218.
Here, we focus on better understanding of the effect of summary statistic selection on ABC performance.
Again, summary statistic selection is an important aspect of ABC's ability to provide adequate model selection capability.
A few recent papers have considered summary statistic selection from the viewpoint of aiming for better inference or better approximation to the full posterior probability [ 18– 218.
To focus on summary statistic selection, we simplify the measurement assumptions and measurement error model used in [ 10] and assume that measurements of { Y 1, Y 2} are available at a sequence of times { t 1, t 2,…, t n }.
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Variables were kept in the model according to the likelihood ratio statistic (forward selection p < 0.05).
The Tajima's D statistic indicates selection when it significantly differs from the neutral expectation of D = 0.
As extensions of Tajima's D, Fu and Li's F* statistic detects selection signatures through the comparison of the number of singleton mutations and the mean pairwise difference between sequences (Fu and Li 1993).
In [ 23], the authors applied probabilistic neural networks (PNNs) to the class prediction of ALL-AML, and achieved 100% prediction accuracy in the test set using the 50-gene predictors derived from cross-validation tests of the training set by means of the signal-to-noise statistic feature selection method.
The obtained curves were then compared with those developed by Giorlandino et al. [ 19] as reference growth curves for the Italian population and those developed by Johnsen et al. [ 20] as reference growth curves for the European population, in order to verify possible differences due to statistic methodology, selection criteria, or, possibly, true genetic variability of the studied population.
In a similar vein, [ 32] proposed a composite test statistic of several selection signature signals to increase power to detect selection.
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