| 期刊全称 | A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem | | 期刊简称 | with Simulations and | | 影响因子2023 | Tejas Desai | | 视频video | http://file.papertrans.cn/142/141491/141491.mp4 | | 发行地址 | Applies aspects of multivariate normality to the concept of hypothesis testing.Introduces a novel multivariate solution to a long-standing statistical problem ?.Includes supplementary material: | | 学科分类 | SpringerBriefs in Statistics | | 图书封面 |  | | 影响因子 | In statistics, the Behrens–Fisher problem is the problem of interval estimation and hypothesis testing concerning the difference between the means of two normally distributed populations when the variances of the two populations are not assumed to be equal, based on two independent samples. In his 1935 paper, Fisher outlined an approach to the Behrens-Fisher problem. Since high-speed computers were not available in Fisher’s time, this approach was not implementable and was soon forgotten. Fortunately, now that high-speed computers are available, this approach can easily be implemented using just a desktop or a laptop computer. Furthermore, Fisher’s approach was proposed for univariate samples. But this approach can also be generalized to the multivariate case. In this monograph, we present the solution to the afore-mentioned multivariate generalization of the Behrens-Fisher problem. We start out by presenting a test of multivariate normality, proceed to test(s) of equality of covariance matrices, and end with our solution to the multivariate Behrens-Fisher problem. All methods proposed in this monograph will be include both the randomly-incomplete-data case as well a | | Pindex | Book 2013 |
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