Hypothesis test on the population mean with various inequality constraints is studied in this paper.The empirical likelihood method is applied to construct test statistics.Limiting distributions of the empirical likel...Hypothesis test on the population mean with various inequality constraints is studied in this paper.The empirical likelihood method is applied to construct test statistics.Limiting distributions of the empirical likelihood ratio test statistics are proven to be a weighted mixture of chi-square distributions.Numerical results are presented to show the validity of the proposed method.展开更多
This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statist...This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statistic under null and nonnull hypotheses.The power of the test is then discussed.Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t(Mt) distribution.展开更多
Second-order stochastic dominance plays an important role in reliability and various branches of economics such as finance and decision-making under risk, and statistical testing for the stochastic dominance is often ...Second-order stochastic dominance plays an important role in reliability and various branches of economics such as finance and decision-making under risk, and statistical testing for the stochastic dominance is often useful in practice. In this paper, we present a test of stochastic equality under the constraint of second-order stochastic dominance based on the theory of empirical processes. The asymptotic distribution of the test statistic is obtained, and a simple method to compute the critical value is derived. Simulation results and real data examples are presented to illustrate the proposed test method.展开更多
基金supported by National Nature Science Foundation of China(Grant No.10731010),National Nature Science Fund for Creative Research Groups(GrantNo.10721101)Key Fund of Yunnan Province(Grant No.2010CC003)
文摘Hypothesis test on the population mean with various inequality constraints is studied in this paper.The empirical likelihood method is applied to construct test statistics.Limiting distributions of the empirical likelihood ratio test statistics are proven to be a weighted mixture of chi-square distributions.Numerical results are presented to show the validity of the proposed method.
基金supported by the National Natural Science Foundation of China under Grant No.11301514National Bureau of Statistics of China under Grant No.2012LZ012
文摘This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statistic under null and nonnull hypotheses.The power of the test is then discussed.Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t(Mt) distribution.
基金This work is supported by Grants from the Natural Science Foundation of China （11271039） Specialized Research Fund for the Doctoral Program of Higher Education+2 种基金 Research Fund of Weifang University （2011Z24） Funding Project of Science and Technology Research Plan of Weifang City （201301019） The Natural Science Foundation of Shandong （ZR2013FL032）.
文摘Second-order stochastic dominance plays an important role in reliability and various branches of economics such as finance and decision-making under risk, and statistical testing for the stochastic dominance is often useful in practice. In this paper, we present a test of stochastic equality under the constraint of second-order stochastic dominance based on the theory of empirical processes. The asymptotic distribution of the test statistic is obtained, and a simple method to compute the critical value is derived. Simulation results and real data examples are presented to illustrate the proposed test method.
