The traditional approaches of modeling and estimation of highly skewed deposits have led to incorrect evaluations, creating challenges and risks in resource management. The low concentration of the rare earth element (REE) deposits, on one hand, and their strategic importance, on the other, enhances the necessity of multivariate modeling of these deposits. The wide variations of the grades and ...

Multivariate control chats are generally used in situations where the simultaneous monitoring or control of two or more related quality characteristics is necessary. In most processes in the real world, distribution of the process characteristics are unknown or at least non-normal, so the non-parametric or distribution-free charts are desirable. Most non-parametric statistical process-control t...

Markov chain Monte Carlo (MCMC) is a simulation method commonly used for estimating expectations with respect to a given distribution. We consider estimating the covariance matrix of the asymptotic multivariate normal distribution of a vector of sample means. Geyer [9] developed a Monte Carlo error estimation method for estimating a univariate mean. We propose a novel multivariate version of Ge...

Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compa...

Simultaneously analyzing multivariate time series provides an insight into underlying interaction mechanisms of cardiovascular system and has recently become an increasing focus of interest. In this study, we proposed a new multivariate entropy measure, named multivariate fuzzy measure entropy (mvFME), for the analysis of multivariate cardiovascular time series. The performances of mvFME, and i...

The tail dependence indexes of a multivariate distribution describe the amount of dependence in the upper right tail or lower left tail of the distribution and can be used to analyze the dependence among extremal random events. This paper examines the tail dependence of multivariate t-distributions whose copulas are not explicitly accessible. The tractable formulas of tail dependence indexes of...

We model the distribution of normalized interpoint distances (IDs) on the minimal spanning tree (MST) using multivariate beta vectors. We define overlapping sums of the components of a Dirichlet distribution to construct multivariate beta distributions. We also use a multivariate normal copula with beta marginals to define beta vectors. Based on the ordered IDs of the MST, we define a multivari...

In this paper kernel estimates of the joint and conditional probability density functions are used to generate synthetic streamflow sequences. Streamflow is assumed to be a Markov process with time dependence characterized by a multivariate probability density function. Kernel methods are used to estimate this multivariate density function. Simulation proceeds by sequentially resampling from th...

Experimenters characterize the behavior of simulation models for data communications networks by measuring multiple responses under selected parameter combinations. The resulting multivariate data may include redundant responses reflecting aspects of a smaller number of underlying behaviors. Reducing the dimension of multivariate responses can reveal the most significant model behaviors, allowi...