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In this study, we propose several simultaneous tests for the mean vector and covariance matrix of multivariate normal data. First of all, we consider the likelihood ratio (LR) test with a limiting distribution, and estimate one using the Monte-Carlo (MC) method for the null distribution. Next, we consider a union-intersection (UI) test by identifying the LR statistic as a product of two functions of random quantities. In addition, we propose simultaneous tests with combination functions. Then, we illustrate our procedure with an example, and compare the efficiency through a simulation study. Finally, we discuss interesting features for the proposed simultaneous tests with related topics and a method for obtaining the LR statistics. [ABSTRACT FROM AUTHOR]

In this paper, we propose several tests for detecting difference in means and variances simultaneously between two populations under normality. First of all, we propose a likelihood ratio test. Then we obtain an expression of the likelihood ratio statistic by a product of two functions of random quantities, which can be used to test the two individual partial hypotheses for differences in means and variances. With those individual partial tests, we propose a union-intersection test. Also we consider two optimal tests by combining thep-values of the two individual partial tests. For obtaining null distributions, we apply the permutation principle with the Monte Carlo approach. Then we compare efficiency among the proposed tests with well-known ones through a simulation study. Finally, we discuss some interesting features related to the simultaneous tests and resampling methods as concluding remarks. [ABSTRACT FROM AUTHOR]

In this study, we propose nonparametric tests using the several quantile statistics simultaneously for the right censored data. First of all, we consider statistics of the quadratic form with estimated covariance matrices. Then we derive the limiting distribution using the large sample approximation theory. Also we consider different forms of statistics such as the maximal and summing types with their limiting distributions. Then we illustrate our procedure with examples and compare performance among tests with empirical powers through a simulation study. Also we comment briefly on some interesting features including re-sampling methods as concluding remarks. Finally in Appendices, we provide proofs for the theoretic results needed for the derivation of the limiting distributions of the proposed test statistics. [ABSTRACT FROM PUBLISHER]