Many biological experiments for measuring the concentration levels of the gene transcripts or protein molecules involve the application of the Polymerase Chain Reaction (PCR) procedure to the gene or protein samples. To better model the results of the these experiments, we propose a new sampling scheme—sampling, amplification, and resampling (SAR)—for generating discrete data, and derive the as...

A number of applications require robust human face recognition under varying environmental lighting conditions and different facial expressions, which considerably vary the appearance of human face. However, in many face recognition applications, only a small number of training samples for each subject are available; these samples are not able to capture all the facial appearance variations. We...

We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators i...

In this article, we obtain an importance resampling formula to reduce the amount of resampling necessary for the construction of bootstrap con dence regions. In the one-dimensional case, the formula reduces to that of Jones (1988) and Do & Hall (1991). However, the methods employed by previous authors are not tamed for direct generalization to multi-dimensional parameters. Therefore no formula ...

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications. This renders inference based ...

When designing programs or software for the implementation of Monte Carlo (MC) hypothesis tests, we can save computation time by using sequential stopping boundaries. Such boundaries imply stopping resampling after relatively few replications if the early replications indicate a very large or very small p-value. We study a truncated sequential probability ratio test (SPRT) boundary and provide ...

Epipolar resampling aims to eliminate the vertical parallax of stereo images. Due to the dynamic nature of the exterior orientation parameters of linear pushbroom satellite imagery and the complexity of reconstructing the epipolar geometry using rigorous sensor models, so far, no epipolar resampling approach has been proposed based on these models. In this paper for the first time it is shown t...

A nonparametric method for resampling scalar or vector-valued time series is introduced. Multivariate nearest neighbor probability density estimation provides the basis for the resampling scheme developed. The motivation for this work comes from a desire to preserve the dependence structure of the time series while bootstrapping (resampling it with replacement). The method is data driven and is...

Resampling techniques are used widely within the ERP community to assess statistical significance and especially in the deception detection literature. Here we argue that because of statistical bias, bootstrap should not be used in combination with methods like peak – to –peak. Instead permutation tests provide a more appropriate alternative. Keywords: bootstrap, permutation, significance testi...