This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kern...

This paper analyses a single server with bulk service queue with general arrival pattern and multiple working vacation period. The model is analyzed by using Embedded Markov Chain technique. The steady state probability distribution at pre arrival epoch and arbitrary epoch are derived and measures like mean queue length are calculated. Finally, through some numerical examples, the parametric ef...

In this paper we propose and examine non–parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi–scale Gabor filter bank. We will demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence off...

OBJECTIVE In recent years, a new type of speaker called the parametric speaker has been used to generate highly directional sound, and these speakers are now commercially available. In our previous study, we verified that the burden of the parametric speaker was lower than that of the general speaker for endocrine functions. However, nothing has yet been demonstrated about the effects of the sh...

Parametric Markov chains have been introduced as a model for families of stochastic systems that rely on the same graph structure, but differ in the concrete transition probabilities. The latter are specified by polynomial constraints for the parameters. Among the tasks typically addressed in the analysis of parametric Markov chains are (1) the computation of closed-form solutions for reachabil...

The notion of data depth has long been in use to obtain robust location and scale estimates in a multivariate setting. The depth of an observation is a measure of its centrality, with respect to a data set or a distribution. The data depths of a set of multivariate observations translates to a centeroutward ordering of the data. Thus, data depth provides a generalization of the median to a mult...

ABSTRACT In this paper, we have used SAS software for the multivariate analysis of repeated measures data due to Grizzel and Allen (1969). We have applied four multivariate methods viz MANOVA, Profile Analysis, non-parametric multisample rank sum test and non-parametric multisample median test to analyse two sets of data. The findings of the study reveal that profile analysis gives similar resu...

Although non-parametric tests have already been proposed for that purpose, statistical significance tests for non-standard measures (different from the classification error) are less often used in the literature. This paper is an attempt at empirically verifying how these tests compare with more classical tests, on various conditions. More precisely, using a very large dataset to estimate the w...

A test for a parametric regression model against a sequence of local alternative is constructed based on an empirical likelihood test statistic that measures the goodness-of-fit between the parametric model and its nonparametric counterpart. To reduce the dependence of the test on a single smoothing bandwidth, the test is formulated by maximizing a standardized version of the empirical likeliho...

Computing optimal stochastic portfolio execution strategies under an appropriate risk consideration presents many computational challenges. Using Monte Carlo simulations, we investigate an approach based on smoothing and parametric rules to minimize mean and Conditional Value-at-Risk (CVaR) of the execution cost. The proposed approach reduces computational complexity by smoothing the nondiffere...