x ξxδx where the sum is over points x of a Poisson point process of intensity λ on a bounded region in d-space, and ξx is a functional determined by the Poisson points near to x, i.e. satisfying an exponential stabilization condition, along with a moments condition (examples include statistics for proximity graphs, germ-grain models and random sequential deposition models). A known general resu...

The numerical computation of a multivariate normal probability is often a diicult problem. This article describes a transformation that simpliies the problem and places it into a form that allows eecient calculation using standard numerical multiple integration algorithms. Test results are presented that compare implementations of two algorithms that use the transformation, with currently avail...

In this paper, a new mixture family of multivariate normal distributions, formed by mixing distribution and skewed distribution, is constructed. Some properties family, such as characteristic function, moment generating the first four moments are derived. The distributions affine transformations canonical forms model also An EM-type algorithm developed for maximum likelihood estimation paramete...

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...

This article discusses estimation of a heteroscedastic multivariate normal mean in terms of the ensemble risk. We first derive the ensemble minimaxity properties of various estimators that shrink towards zero. We then generalize our results to the case where the variances are given as a common unknown but estimable chi-squared random variable scaled by different known factors. We further provid...

We present an eecient and accurate method to evaluate multivariate normal probabilities with arbitrary singular correlation matrices. The new method is applied to the construction of simultaneous conndence intervals and simultaneous all pairwise conndence intervals for multinomial proportions when the sample size is suuciently large.

We present methods for the computation of multivari-ate normal probabilities on parallel/ distributed systems. After a transformation of the initial integral, an approximation can be obtained using Monte-Carlo or quasi-random methods. We propose a meta-algorithm for asynchronous sampling methods and derive eecient parallel algorithms for the computation of MVN distribution functions, including ...