Born from empirical observations in nonuniformly sampled multidimensional NMR data relating to gaps between sampled points, the Poisson-gap sampling method has enjoyed widespread use in biomolecular NMR. While the majority of nonuniform sampling schemes are fully randomly drawn from probability densities that vary over a Nyquist grid, the Poisson-gap scheme employs constrained random deviates t...

A number of techniques for probability sampling without replacement (SWOR) have been suggested, although it is not clear which method is consistently superior in terms of statistical efficiency. Rao and Bayless (1969) empirically studied the stability of estimators of the population total for a variety of methods of unequal probability SWOR when selecting two units per stratum. One of their maj...

In ecology, as in other research fields, efficient sampling for population estimation often drives sample designs toward unequal probability sampling, such as in stratified sampling. Design based statistical analysis tools are appropriate for seamless integration of sample design into the statistical analysis. However, it is also common and necessary, after a sampling design has been implemente...

We present a cluster-based sampling and ensemble method to learn from large, imbalanced data set for bleeding detection in CE videos. Our method selects training examples randomly according to the data distributions derived from clustering. Multiple training sets are created such that data balance is restored. The sampling probability is proportional to the cluster distribution, and within each...

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional dist...

• We present a new adaptive sampling method for statistical quality control. In this method, called LSI (Laplace sampling intervals), we use the probability distribution function of the Laplace standard distribution to obtain the sampling instants, depending on a k parameter that allows control of sampling costs. Several algebraic expressions concerning the statistical properties of the LSI met...