This paper reviews some of the major issues associated with the statistical evaluation of Human Identification algorithms, emphasizing comparisons between algorithms on the same set of sample images. A general notation is developed and common performance metrics are defined. A simple success/failure evaluation methodology where recognition rate depends upon a binomially distributed random varia...

Dedicated to the 70th birthday of Johann Pfanzagl Summary. Along the lines of Pfanzagl's work the testing theory for the non-parametric null hypothesis of symmetry (including matched pairs) is developed. The testing problem is typically given by a skew symmetric statistical functional which seems to be adequate for the nonparametric world. Under mild regularity assumptions asymptotically maximi...

Many important questions in neuroscience are about interactions between neurons or neuronal groups. These interactions are often quantified by coherence, which is a frequency-indexed measure that quantifies the extent to which two signals exhibit a consistent phase relation. In this paper, we consider the statistical testing of the difference between coherence values observed in two experimenta...

Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence. We present a case study which involves a set of techniques in classification tasks and we study a set of ...

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The softw...

Recently, Neural Networks became a popular method in geosciences in the context of pattern recognition problems. They can be used for classification of well logs, for image processing, for anomaly detection and similar problems. In the past, such problems were mainly solved by statistical approaches. However parametric statistical classification methods suffer from strong assumptions and nonpar...