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Showing 3 results for Hypothesis Test
Hamid Esmaili, Mina Towhidi, Seyd Rooalla Roozgar, Mehdi Amiri,
Volume 5, Issue 1 (9-2011)
Abstract
Usually, in testing hypothesis a p_value is used for making decision. Would p_value be the best measure to accept or reject the null hypothesis? Would it be possible to have a better measure than the ordinary p_value? In this paper, hypothesis testing has been considered not as a choice to make decision but as an estimating problem to possible accuracy of a given set, labeled by Θ_0 and p_value would be used as an estimator to possible accuracy of Θ_0 Real numbers as a parametric space has been usually accepted by researcher although the parametric space has been limited in many of applications. A measure named as modified p_value which functions more better than usual p_value in bounded parametric space, would be introduced in normal distribution of one-side and two-side testing.
Saba Aghadoust, Kamel Abdollahnezhad, Farhad Yaghmaei, Ali Akbar Jafari,
Volume 9, Issue 1 (9-2015)
Abstract
The log-normal distribution is used to describe the positive data that has skewed distribution with small mean and large variance. This distribution has application in many sciences for example medicine, economics, biology and alimentary science, etc. Comparison of means of several log-normal populations always has been in focus of researchers, but their test statistics are not easy to derive or extremely complicated for this comparisons. In this paper, the size and power of different testing methods including F-test, likelihood ratio test, generalized p-value approach and computational approach test are compared in a simulation study.
Abouzar Bazyari,
Volume 10, Issue 1 (8-2016)
Abstract
Hypothesis testing the homogeneity of means of k univariate normal populations against the hypothesis of one sided ordered means with unknown and equal variances is considered. A new completely method to find the uniformly most powerful test at significance level α is presented based on the multivariate t distribution. Since for more than two populations finding the null distribution of test statistic is not easy, the power of test is computed and then the critical values of test statistic for different significance levels obtained. This testing method is used for real examples. Also testing homogeneity of k mean vectors against two sided ordered mean vectors of multivariate normal populations is considered. Using Monte Carlo simulation the values of classical power of test for two bivariate and trivariate normal distributions at different significance levels are compared.
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