Fithian and Hastie (2014) proposed a new sampling scheme called local case-control (LCC) that achieves stability efficiency by utilizing clever adjustment pertained to the logistic model. It is particularly useful for classification with large imbalanced data. This paper proposes more general based on working principle data points deserve higher probability if they contain information or appear...

This paper exploits recent developments in sparse approximation and compressed sensing to efficiently perform the direction finding. The new method is proposed based on unimodal characteristic of antenna pattern and sparse property of received data. Unlike the conventional methods based peak-searching and symmetric constraint, the sparse reconstruction algorithm requires less pulse and takes ad...

Dispersal among local communities can have a variety of effects on species composition and diversity at local and regional scales. Local conditions (e.g., resource and predator densities) can have independent effects, as well as interact with dispersal, to alter these patterns. Based on metacommunity models, we predicted that local diversity would show a unimodal relationship with dispersal fre...

The mechanisms leading to structure in local assemblages are controversial. On the one hand, assemblage structure is thought to be the outcome of local interactions determined by the properties of species and their responses to the local environment. Alternatively, this structure has been shown to be an emergent property of assemblages of identical individuals or of random sampling of a regiona...

We introduce a new Empirical Bayes approach for large-scale hypothesis testing, including estimating false discovery rates (FDRs), and effect sizes. This approach has two key differences from existing approaches to FDR analysis. First, it assumes that the distribution of the actual (unobserved) effects is unimodal, with a mode at 0. This "unimodal assumption" (UA), although natural in many cont...

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian processes (GP), that does not rely on linearizations or unimodal Gaussian approximations of the belief. The algorithm can be seen as a combination of a sampling-ba...