Search

In this paper we introduce the sequential precedence test (SPT) for testing the equality of two continuous distribution functions, against the stochastically ordered alternative. This procedure replaces the classical precedence test (PT), used in life-testing experiments, by a sequence of tests which are applied at the failure times of one of the samples. This allows the possibility of stopping the experiment earlier than the PT. By means of extensive Monte Carlo simulations and real data, we show that the proposed methodology results in substantial saving of experimental time and cost, without compromising in power. Algorithms for the implementation of the SPT are included. [ABSTRACT FROM AUTHOR]

We describe a method for analyzing the principal modes of geometric variability of images. For this purpose, we propose a general framework based on the use of deformation operators for modeling the geometric variability of images around a reference mean pattern. In this setting, we describe a simple algorithm for estimating the geometric variability of a set of images. Some numerical experiments on real data are proposed for highlighting the benefits of this approach. The consistency of this procedure is also analyzed in statistical deformable models. [ABSTRACT FROM AUTHOR]

The availability of high-throughput parallel methods for sequencing microbial communities is increasing our knowledge of the microbial world at an unprecedented rate. Though most attention has focused on determining lower-bounds on the α-diversity i.e. the total number of different species present in the environment, tight bounds on this quantity may be highly uncertain because a small fraction of the environment could be composed of a vast number of different species. To better assess what remains unknown, we propose instead to predict the fraction of the environment that belongs to unsampled classes. Modeling samples as draws with replacement of colored balls from an urn with an unknown composition, and under the sole assumption that there are still undiscovered species, we show that conditionally unbiased predictors and exact prediction intervals (of constant length in logarithmic scale) are possible for the fraction of the environment that belongs to unsampled classes. Our predictions are based on a poissonization argument, which we have implemented in what we call the Embedding algorithm. In fixed i.e. non-randomized sample sizes, the algorithm leads to very accurate predictions on a sub-sample of the original sample. We quantify the effect of fixed sample sizes on our prediction intervals and test our methods and others found in the literature against simulated environments, which we devise taking into account datasets from a human-gut and -hand microbiota. Our methodology applies to any dataset that can be conceptualized as a sample with replacement from an urn. In particular, it could be applied, for example, to quantify the proportion of all the unseen solutions to a binding site problem in a random RNA pool, or to reassess the surveillance of a certain terrorist group, predicting the conditional probability that it deploys a new tactic in a next attack.