New divergence measures are introduced for change detection and discrimination of stochastic signals (time series) on the basis of parametric filtering — a technique that combines parametric linear filtering with correlation characterization. The sensitivity of these divergence measures is investigated using local curvatures under additive and multiplicative spectral departure models. It is fou...

Finding the relationships between information measures and statistical constants leads to the applicability of information theory to the field of statistics. In the existing literature of information theory, there are many well known information theoretic measures, each with its own merits, limitations and areas of applications. In the present communication, we have developed new generalized pa...

Purpose: Spectral analysis of heart rate variability (HRV) constitutes a useful tool for the evaluation of autonomic function. However, it is difficult to compare the published data because different mathematical approaches for the calculation of the frequency bands are applied. Our aim was to compare the HRV frequency domain parameters obtained by application of 2 parametric and 2 non-parametr...

We consider the problem of jointly inferring the M -best diverse labelings for a binary (high-order) submodular energy of a graphical model. Recently, it was shown that this problem can be solved to a global optimum, for many practically interesting diversity measures. It was noted that the labelings are, so-called, nested. This nestedness property also holds for labelings of a class of paramet...

Parametric stochastic frontier models have a long history in applied production economics, but the class of tractible parametric models is relatively small. Consequently, researchers have recently considered non–parametric alternatives such as kernel density estimators, functional approximations, and data envelopment analysis (DEA). The purpose of this paper is to present an information theoret...

This paper discusses a local parametric modeling by the use of U-divergence in a statistical pattern recognition. The class of U-divergence measures commonly has an empirical loss function in a simple form including Kullback-Leibler divergence, the power divergence and mean squared error. We propose a minimization algorithm for parametric models of sequentially increasing dimension by incorpora...

Growth is a key cellular phenotype relevant in areas ranging from microbiology to cancer biology. Hence, quantitative measures of growth must be accurately estimated. Historically, many parametric models have been proposed, reviewed in ([1]). However, in our experience, growth curves rarely follow these idealistic behaviours. In practice, non-parametric models, in which curves are simply smooth...

A growing number of studies endeavor to reveal periodicities in sensory and cognitive functions, by comparing the distribution of ongoing (pre-stimulus) oscillatory phases between two (or more) trial groups reflecting distinct experimental outcomes. A systematic relation between the phase of spontaneous electrophysiological signals, before a stimulus is even presented, and the eventual result o...

In the redundant target effect, participants respond faster with two (redundant) targets. We compared the magnitude of this effect in younger and older adults, with and without distractors, in a simple visual-detection task. We employed additional measures that allow non-parametric assessment of performance (Townsend's capacity coefficient) and parametric estimates (Linear Ballistic Accumulator...