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Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research.
In this review, we illustrate recent developments in this area, and outline a roadmap for improved selection inference.
J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact-post selection inference with application to the lasso, 2014 MSM Consortium Meeting, Bethesda, MD, Sep 3 5, 2014.
: Individuals less suited to the environment are less likely to survive and less likely to reproduce; individuals more suited to the environment are more likely to survive and more likely to reproduce and leave their heritable traits to future generations, which produces the process of natural selection (inference).
For example, positive selection inference was found to be more accurate when unreliable MSA regions were filtered out.
To develop selection inference methods applicable under strong interference, we have to extend our picture of the genome state to polymorphism spectra.
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The allele age also is an important parameter in selection inferences because it can provide information regarding the origin of a particular phenotype associated with the allele.
For comparisons of diversity in the two species, we used all 105 coding loci, but for analyses that involved pooling data from all loci (such as comparisons of the site frequency spectrum, demographic, and selection inferences), we excluded five loci (CG1619, CG12717, CG32702, CG32790, and CG12239) that had sample sizes <20 in one or both species.
J. D. Lee, D. L. Sun, Y. Sun, and J. E. Taylor, Exact post-selection inference, with application to the lasso, Annals of Statistics, to appear.
Unfortunately, as already remarked, the intricated phylogenetic history of the A. gambiae complex hinder the interpretation of our results from selection-inference methods.
However, the application of selection-inference methods in closely related taxa, such as those of the A. gambiae complex, imposes some limitations and a critical evaluation of the results [ 15, 16].
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