We consider a two-dimensional model of cell-to-cell spread of HIV-1 in tissue cultures, assuming that infection is spread directly from infected cells to healthy cells and neglecting the effects of free virus. The intracellular incubation period is modeled by a gamma distribution and the model is a system of two differential equations with distributed delay, which includes the differential equa...

Gene expression data are influenced by multiple biological and technological factors leading to a wide range of dispersion scenarios, although skewed patterns are not commonly addressed in microarray analyses. In this study, the distribution pattern of several human transcriptomes has been studied on free-access microarray gene expression data. Our results showed that, even in previously normal...

Background: In light of the latest global financial crisis and the ongoing sovereign debt crisis, accurate measuring of market losses has become a very current issue. One of the most popular risk measures is Value-at-Risk (VaR). Objectives: Our paper has two main purposes. The first is to test the relative performance of selected GARCH-type models in terms of their ability of delivering volatil...

In this paper, the S&P 500 stock index is studied for its time varying volatility and stylized facts. The ARMA mean equation with asymmetric power ARCH errors is used to model the series correlations and the conditional heteroscadesticity in the asset returns. The conditional distributions of the standardized residuals are assumed to be the normal distribution, the t distribution or the skew-t ...

Although pressured metallic glass (MG) has been reported in the literature; there are few studies focusing on pressure effects on the structure; dynamics and its plastic deformation. In this paper; we report on and characterize; via molecular dynamics simulation, the structure and dynamics heterogeneity of pressured MGs, and explore a causal link between local structures and plastic deformation...

1 Review of the Bayes Algorithm Last time we talked about the Bayes algorithm, in which we give priors π i to each expert. The algorithm maintains weights w t,i for each expert. The π i values serve as the initial weights for the experts. Experts predict the distributions p t,i over the same set X, and the algorithm predicts the q t distribution as a mixture of those distributions. To restate i...

For probability distributions on IR, a detailed study of the breakdown properties of some multivariate M-functionals related to Tyler’s (1987a) “distribution-free” M-functional of scatter is given. These include a symmetrized version of Tyler’s M-functional of scatter, and the multivariate t M-functionals of location and scatter. It is shown that for “smooth” distributions, the (contamination) ...

The commonly made assumption of Gaussian noise is an approximation to reality. In this paper, influence function, an analysis tool in robust statistics, is used to formulate a recursive solution for the filtering of the ARMAX process with generalized t-distribution noise. By being a superset encompassing Gaussian, uniform, t, and double exponential distributions, generalized t-distribution has ...

BACKGROUND Random-effects meta-analysis is commonly performed by first deriving an estimate of the between-study variation, the heterogeneity, and subsequently using this as the basis for combining results, i.e., for estimating the effect, the figure of primary interest. The heterogeneity variance estimate however is commonly associated with substantial uncertainty, especially in contexts where...

One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account the uncertainty due to the fact that the heteroge...