Random orthogonal matrix (ROM) simulation is a very fast procedure for generating multivariate random samples that always have exactly the same mean, covariance and Mardia multivariate skewness and kurtosis. This paper investigates how the properties of parametric, data-specific and deterministic ROM simulations are influenced by the choice of orthogonal matrix. Specifically, we consider how cy...

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the mu...

MOTIVATION Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. RESULTS Here ...

multivariate process capability indices (mpci) show how well a manufacturing process can meet specifica-tion limits when quality characteristics enclose a relative correlation. process capability is an important and commonly used metric for assessing and improving the quality of a production process. when quality charac-teristics of a product are correlated then an attractive comes close to mpc...

A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. In this paper we propose multivariate out-of-sample tests for Granger causality. The performance of the out-of-sample tests is measured by a simulatio...

This paper proposes a semiparametric methodology for modeling multivariate and conditional distributions. We first build a multivariate distribution whose dependence structure is induced by a Gaussian copula and whose marginal distributions are estimated nonparametrically via mixtures of B-spline densities. The conditional distribution of a given variable is obtained in closed form from this mu...

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3 variance-covariance structures. The results from a full-cross...

Process-Oriented Basis Representation (POBREP) is an effective method of multivariate statistical process control that is seldom used in manufacturing. Rather than monitoring individual process variables, POBREP seeks to identify specific causes of production problems and map those into a basis matrix. These patterns can then be monitored with single-variable SPC techniques or traditional multi...

Outlier detection statistics based on two models, the case-deletion model and the mean-shift model, are developed in the context of a multivariate linear regression model. These are generalizations of the univariate Cook’s distance and other diagnostic statistics. Approximate distributions of the proposed statistics are also obtained to get suitable cutoff points for significance tests. In addi...

We show how it is possible to generate multivariate data which have moments arbitrary close to the desired ones. They are generated as linear combinations of variables with known theoretical moments. It is shown how to derive the weights of the linear combinations in both the univariate and the multivariate setting. The use in bootstrapping is discussed and examplified with an Monte Carlo simul...