Spurious regression is a serious problem in empirical research when time series has unit roots. This problem might exist when we apply a regression procedure for pooling time series and cross-section data. There are several models to pool time series and cross-section data in regression context, e.g. fixed effect or random effect model. Recently, Entorf [1997] and Kao [1999] also pointed out th...

The need for quantifying the effect of resource pooling on performance of multi-processing systems arises frequently in the design of a variety of manufacturing, communication, and service systems. In this paper, we examine the effect of resource pooling and assess its impact on system performance. In particular, we provide performance bounds on the effectiveness of several pooling scenarios an...

MOTIVATION Many biomedical experiments are carried out by pooling individual biological samples. However, pooling samples can potentially hide biological variance and give false confidence concerning the data significance. In the context of microarray experiments for detecting differentially expressed genes, recent publications have addressed the problem of the efficiency of sample pooling, and...

We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a p...

Assays can be so expensive that interesting hypotheses become impractical to study epidemiologically. One need not, however, perform an assay for everyone providing a biological specimen. We propose pooling equal-volume aliquots from randomly grouped sets of cases and randomly grouped sets of controls, and then assaying the smaller number of pooled samples. If an effect modifier is of concern, ...

We introduce Eigen Evolution Pooling, an efficient method to aggregate a sequence of feature vectors. Eigen evolution pooling is designed to produce compact feature representations for a sequence of feature vectors, while maximally preserving as much information about the sequence as possible, especially the temporal evolution of the features over time. Eigen evolution pooling is a general pool...

Object recognition is difficult because the appearance of an object changes in many different ways. To recognize objects robustly, one needs representations that are constant despite those changes. Such invariant representations can be obtained by features with low sensitivity to various visual transformations. Spatial pooling is a widely used technique for extracting invariant features from im...