SummaryThe ordinary Bayes information criterion is too liberal for model selection when the model space is large. In this article, we re-examine the Bayesian paradigm for model selection and propose an extended family of Bayes information criteria. The new criteria take into account both the number of unknown parameters and the complexity of the model space. Their consistency is established, in particular allowing the number of covariates to increase to infinity with the sample size. Their performance in various situations is evaluated by simulation studies. It is demonstrated that the extended Bayes information criteria incur a small loss in the positive selection rate but tightly control the false discovery rate, a desirable property in many applications.The extended Bayes information criteria are extremely useful for variable selection in problems with a moderate sample size but a huge number of covariates, especially in genome-wide association studies, which are now an active area in genetics research.

The Asian tiger mosquito, Aedes albopictus, is a highly successful invasive species that transmits a number of human viral diseases, including dengue and Chikungunya fevers. This species has a large genome with significant population-based size variation. The complete genome sequence was determined for the Foshan strain, an established laboratory colony derived from wild mosquitoes from southeastern China, a region within the historical range of the origin of the species. The genome comprises 1,967 Mb, the largest mosquito genome sequenced to date, and its size results principally from an abundance of repetitive DNA classes. In addition, expansions of the numbers of members in gene families involved in insecticide-resistance mechanisms, diapause, sex determination, immunity, and olfaction also contribute to the larger size. Portions of integrated flavivirus-like genomes support a shared evolutionary history of association of these viruses with their vector. The large genome repertory may contribute to the adaptability and success of Ae. albopictus as an invasive species.

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SummaryComputing profile empirical likelihood function is a key step in applications of empirical likelihood which involves constrained maximization. However, in some situations, solutions to the corresponding constraints may not exist. In this case, the convention is to assign a zero value to the profile empirical likelihood. This convention has at least two limitations.First, it is numerically difficult to determine the non-existence of any solution; Second, it provides no information on the relative plausibility of these parameter values. In this paper, we use a novel adjustment to the empirical likelihood so that the new method retains all the optimal properties, while guarantees a sensible value at any parameter point. Coupled with this adjustment, we introduce an iterative algorithm with guaranteed convergence.Our simulation indicates that the adjusted empirical likelihood is much faster to compute.The confidence regions constructed via the adjusted empirical likelihood are found to have closer to nominal coverage probabilities without resorting to more complex procedures such as Bartlett correction or bootstrap caliberation. Through some application examples, the method is also shown to be very effective in solving some practical problems associated with the use of empirical likelihood.