Pooling biospecimens is a well accepted sampling strategy in biomedical research to reduce study cost of measuring biomarkers, and has been shown in the case of normally distributed data to yield more efficient estimation. In this paper we examine the efficiency of pooling, in the context of information matrix related to estimators of unknown parameters, when the biospecimens being pooled yield...

Case-control association studies often suffer from population stratification bias. A previous triple combination strategy of stratum matching, genomic controlling, and multiple DNA pooling can correct the bias and save genotyping cost. However the method requires researchers to prepare a multitude of DNA pools-more than 30 case-control pooling sets in total (polyset). In this paper, the authors...

In this paper we analyze the problem of opinion pooling. We introduce a divergence minimization framework to solve the problem of standard opinion pooling. Our results show that various existing pooling mechanisms like LinOp and LogOp are an special case of this framework. This framework is then extended to address the problem of generalized opinion pooling. We show that this framework does sat...

The paper compares different approaches to regional frequency analysis with the main focus on the implementation of the region-of-influence (ROI) technique for the modelling of probabilities of heavy precipitation amounts in the area of the Western Carpathians. Unlike the conventional regional frequency analysis where the at-site design values are estimated within a fixed pooling group (region)...

Risk-adjusted capitation payments (RACPs) to competing health insurers are an essential element of market-oriented health care reforms in many countries. RACPs based on demographic variables only are insufficient, because they leave ample room for cream skimming. However, the implementation of improved RACPs does not appear to be straightforward. A solution might be to supplement imperfect RACP...

Many sequence learning tasks require the localization of certain events in sequences. Because it can be expensive to obtain strong labeling that specifies the starting and ending times of the events, modern systems are often trained with weak labeling without explicit timing information. Multiple instance learning (MIL) is a popular framework for learning from weak labeling. In a common scenari...