Cluster sampling has recently been used to estimate the mortality in various conflicts around the world. The Burnham et al. (2006) study on Iraq employs a new variant of this cluster sampling methodology. The stated methodology of Burnham et al. (2006) is to (1) select a random main street, (2) choose a random cross street to this main street, and (3) select a random household on the cross stre...

If the dataset available to machine learning results from cluster sampling, the usual cross-validation error rate estimate can lead to biased and misleading results. An adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random sampling hypothesis, are compared to the true value. Th...

The practice of grouping the elements of the universe of study into clusters and sampling the clusters is a common feature of sample designs. Dealing with clusters of elements ahnost invariably increases the sampling error of the statistics estimated from the sample. However, clustering usually reduces the per-unit costs of the sampling and data collection. As the cluster size is increased, the...

This paper introduces an external memory parallel sorting algorithm in a multiprocessor architecture. The overall goal is to choose p − 1 partitioning elements so that the final p sorted files, one per processor, are of roughly equal size. It first determines a sample of splitters by either regular sampling or random sampling techniques. Then each data file at each processor is separated accord...

Scheduling diverse applications in large, shared clusters is particularly challenging. Recent research on cluster management focuses either on scheduling speed, using sampling techniques to quickly assign tasks to resources, or on scheduling quality, using centralized algorithms that examine the cluster state to find the most suitable resources that improve both task performance and cluster uti...