Multivariate Fréchet copulas and conditional value-at-risk.
Multivariate Gamma Distributions with One-Factorial Accompanying Correlation Matrices and Applications to the Distribution of the Multivariate Range.
Multivariate measures of concordance for copulas and their marginals
Building upon earlier work in which axioms were formulated for multivariate measures of concordance, we examine properties of such measures. In particular,we examine the relations between the measure of concordance of an n-copula and the measures of concordance of the copula’s marginals.
Multivariate models with constraints confidence regions
In multivariate linear statistical models with normally distributed observation matrix a structure of a covariance matrix plays an important role when confidence regions must be determined. In the paper it is assumed that the covariance matrix is a linear combination of known symmetric and positive semidefinite matrices and unknown parameters (variance components) which are unbiasedly estimable. Then insensitivity regions are found for them which enables us to decide whether plug-in approach can...
Multivariate multiple comparisons with a control in elliptical populations
The approximate upper percentile of Hotelling's T²-type statistic is derived in order to construct simultaneous confidence intervals for comparisons with a control under elliptical populations with unequal sample sizes. Accuracy and conservativeness of Bonferroni approximations are evaluated via a Monte Carlo simulation study. Finally, we explain the real data analysis using procedures derived in this paper.
Multivariate Multistage Methodologies for Simultaneous All Pairwise Comparisons.
Multivariate negative binomial distributions generated by multivariate exponential distributions
We define a multivariate negative binomial distribution (MVNB) as a bivariate Poisson distribution function mixed with a multivariate exponential (MVE) distribution. We focus on the class of MVNB distributions generated by Marshall-Olkin MVE distributions. For simplicity of notation we analyze in detail the class of bivariate (BVNB) distributions. In applications the standard data from [2] and [7] and data concerning parasites of birds from [4] are used.
Multivariate probability integral transformation: application to maximum likelihood estimation.
Sea (X1, X2) un vector aleatorio con una función de distribución F. La transformación integral de la probabilidad (pit) es la variable aleatoria unidimensional P2 = F(X1, X2). La expresion de su función de distribución, y un algoritmo de simulación en términos de la función cuantil, dada por Chakak et al [2000], cuando la distribución es absolumente continua, son extendidas a distribuciones que pueden tener singularidades. La estimación de máxima verosimilitud del parámetro de dependencia basada...
Multivariate regression model with constraints
Multivariate skewness and kurtosis for singular distributions.
In multivariate analysis it is generally assumed that the observations are normally distributed. It was Mardia ([1] to [5]), who first introduced measures of multivariate skewness and kurtosis; these statistics are affine invariant and can be used for testing multivariate normality. Skewness and kurtosis tests remain among the most powerful, general and easy to implement. In this paper we show some properties of these statistics when population distribution is singular.
Multivariate statistical models; solvability of basic problems
Multivariate models frequently used in many branches of science have relatively large number of different structures. Sometimes the regularity condition which enable us to solve statistical problems are not satisfied and it is reasonable to recognize it in advance. In the paper the model without constraints on parameters is analyzed only, since the greatness of the class of such problems in general is out of the size of the paper.
Multivariate statistical pattern recognition with nonreduced dimensionality
Multivariate Survival Functions Characterized by Constant Product of Mean Remaining Lives and Hazard Rates.