Modeling data with multivariate count responses is a challenge problem due to the discrete nature of the responses. Such data are commonly observed in many applications such as biology, epidemiology and social studies. The existing methods for univariate count response cannot be easily extended to the multivariate situation since the dependency among multiple responses needs to be properly acco...

We investigate tests to detect general nonlinear behavior in a vector time series. The proposed tests are multivariate extensions of the univariate nonlinearity tests of Keenan (1985) and Tsay (1986). Simulation results show that, in general, the multivariate tests are more powerful than their univariate counterparts, especially for series having nonlinear structure that involves several compon...

Canonical correlation analysis has been widely used in the literature to identify the underlying structure of a multivariate linear time series. Most of the studies assume that the innovations to the multivariate system are Gaussian. On the other hand, there are many applications in which the normality assumption is either questionable or clearly inadequate. For example, most empirical time ser...

We propose two new types of nonparametric tests for investigating multivariate regression functions. The tests are based on cumulative sums coupled with either minimum volume sets or inverse regression ideas; involving no multivariate nonparametric regression estimation. The methods proposed facilitate the investigation for different features such as if a multivariate regression function is (i)...

It is challenging to simulate large-scale or fine-resolution multivariate three-dimensional (3D) cross-correlated conditional random fields because of computational issues such as inverting, storing Cholesky decomposition large correlation matrices. Recently, an efficient univariate 3D field simulation method was developed based on the separability assumption autocorrelation functions in vertic...

In practice, multivariate dependencies between extreme risks are often only assessed in a pairwise way. We propose a test for detecting situations when such pairwise measures are inadequate and give incomplete results. This occurs when a significant portion of the multivariate dependence structure in the tails is of higher dimension than two. Our test statistic is based on a decomposition of th...

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the inf...

Generating samples from multivariate distributions efficiently is an important task in Monte Carlo integration and many other stochastic simulation problems. Markov chain Monte Carlo has been shown to be very efficient compared to “conventional methods”, especially when many dimensions are involved. In this article we propose a Hit-and-Run sampler in combination with the Ratio-of-Uniforms metho...

The effect of dependence is mostly ignored in queueing systems due to the difficulty in its analysis; the existing studies about dependent queueing systems have restrictive assumptions, such as considering only the M/M/1 queue with order of dependence lag-1 in interarrival and service times. In this paper, we conduct a simulation study to observe the impact of dependence in a single server queu...

The goal of Phase I monitoring of multivariate data is to identify multivariate outliers and step changes so that the estimated control limits are sufficiently accurate for Phase II monitoring. High breakdown estimation methods based on the minimum volume ellipsoid (MVE) or the minimum covariance determinant (MCD) are well suited to detecting multivariate outliers in data. However, they are dif...