The evolution of VLSI chips towards larger die size, smaller feature size and faster clock speed makes the clock distribution an increasingly important issue. In this paper, we propose a new clock distribution network (CDN), namely Variant X-Tree, based on the idea of X-Architecture proposed recently for efficient wiring within VLSI chips. The Variant X-Tree CDN keeps the nice properties of equ...

Let X1;X2;...;Xn have a jointly multivariate exchangeable normal distribution. In this work we investigate another proof of the independence of X and S2 using order statistics. We also assume that (Xi ; Yi); i =1; 2;...; n; jointly distributed in bivariate normal and establish the independence of the mean and the variance of concomitants of order statistics.

The multivariate Student-t copula family is used in statistical finance and other areas when there is tail dependence in the data. It often is a good-fitting copula but can be improved on when there is tail asymmetry. Multivariate skew-t copula families can be considered when there is tail dependence and tail asymmetry, and we show how a fast numerical implementation for maximum likelihood esti...

Complex parameters, such as poverty indicators, are usually difficult to predict in small area estimation (SAE). Elbers et al. (2003) have proposed an empirical semi-parametric method for dealing with poverty indices in SAE. This method, commonly called the ELL method, consists of drawing from the empirical residuals to reconstitute the entire census. After simulating the census, any complex pa...

The results of Waldman (1982) on the Normal-Half Normal stochastic frontier model are generalized using the theory of the Dirac delta (Dirac, 1930), and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and "wrong skew" results are derived or simulated for c...

We extend the Gaussian scale mixture model of dependent subspace source densities to include non-radially symmetric densities using Generalized Gaussian random variables linked by a common variance. We also introduce the modeling of skew in source densities and subspaces using a generalization of the Normal Variance-Mean mixture model. We give closed form expressions for subspace likelihoods an...

The skew t-distribution is a flexible model able to deal with data whose distribution show deviations from normality. It includes both the skew normal and the normal distributions as special cases. Inference for the skew t-model becomes problematic in these cases because the expected information matrix is singular and the parameter corresponding to the degrees of freedom takes a value at the bo...

We explore extremal properties of a family of skewed distributions extended from the multivariate normal distribution by introducing a skewing function π . We give sufficient conditions on the skewing function for the pairwise asymptotic independence to hold. We apply our results to a special case of the bivariate skew-normal distribution and finally support our conclusions by a simulation stud...

Finite mixture models have been widely used to model and analyze data from a heterogeneous populations. Moreover, of this kind can be missing or subject some upper and/or lower detection limits because the constraints experimental apparatuses. Another complication arises when measures each population depart significantly normality, such as asymmetric behavior. For structures, we propose robust ...