Assessing whether two models are statistically significantly different from each other is a very important step in research, although it has unfortunately not received enough attention in the field of person authentication. Several performance measures are often used to compare models, such as half total error rates (HTERs) and equal error rates (EERs), but most being aggregates of two measures...

The comparison of homologous proteins from different species is a first step toward a function assignment and a reconstruction of the species evolution. Though local alignment is mostly used for this purpose, global alignment is important for constructing multiple alignments or phylogenetic trees. However, statistical significance of global alignments is not completely clear, lacking a specific...

Clinical & Investigative Medicine (CIM) is receiving an increasing number of reports of candidate-based association studies. The track record of such studies in the past has been poor: numerous genetic associations reported from candidate gene studies have not been replicated in later studies.1 The rise of the genomewide association study (GWAS) is changing this situation. A well-designed GWAS ...

The results of the MUC-6 evaluation must be analyzed to determine whether close scores significantl y distinguish systems or whether the differences in those scores are a matter of chance. In order to do such an analysis , a method of computer intensive hypothesis testing was developed by SAIC for the MUC-3 results and has been use d for distinguishing MUC scores since that time . The implement...

When assessing map accuracy, confusion matrices are frequently statistically compared using kappa. While kappa allows individual matrix categories to be analyzed with respect to either omission or commission error rates, kappa is not used to compare individual matrix categories with respect to both rates concurrently. When this concurrent comparison is desired, the ma trices are typically norma...

Biclustering (also known as submatrix localization) is a problem of high practical relevance in exploratory analysis of high-dimensional data. We develop a framework for performing statistical inference on biclusters found by score-based algorithms. Since the bicluster was selected in a data dependent manner by a biclustering or localization algorithm, this is a form of selective inference. Our...

Algorithms that compare two proteins or DNA sequences and produce an alignment of the best matching segments are widely used in molecular biology. These algorithms produce scores that when comparing random sequences of length n grow proportional to n or to log(n) depending on the algorithm parameters. The Azuma-Hoeffding inequality gives an upper bound on the probability of large deviations of ...