Approximate Incremental Value-at-Risk formulae provide an easy-to-use preliminary guideline for risk allocation. Both the cases of risk adding and risk pooling are examined and beta-based formulae achieved. Results highlight how much the conditions for adding new risky positions are stronger than those required for risk pooling. JEL classification: C13; D81; G11; G12.

For case-control studies that rely on expensive assays for biomarkers, specimen pooling offers a cost-effective and efficient way to estimate individual-level odds ratios. Pooling helps to conserve irreplaceable biospecimens for the future, mitigates limit-of-detection problems, and enables inclusion of individuals who have limited available volumes of biospecimen. Pooling can also allow the st...

For case-control studies that rely on expensive assays for biomarkers, specimen pooling offers a cost-effective and efficient way to estimate individual-level odds ratios. Pooling helps to conserve irreplaceable biospecimens for the future, mitigates limit-of-detection problems, and enables inclusion of individuals who have limited available volumes of biospecimen. Pooling can also allow the st...

We present a stylized model for analyzing the e®ect of product variety on supply-chain performance for a supply chain with a single manufacturer and multiple retailers. The manufacturer produces multiple products on a shared resource with limited capacity and the e®ect of changeovers on supply-chain cost is due primarily to setup time rather than setup cost. We show that the expected replenishm...

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advoc...

With the increase of available time series data, predicting their class labels has been one most important challenges in a wide range disciplines. Recent studies on classification show that convolutional neural networks (CNN) achieved state-of-the-art performance as single classifier. In this work, pointing out global pooling layer is usually adopted by existing CNN classifiers discards tempora...