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Titlebook: A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem; with Simulations and Tejas Desai Book 2013 The Author 2013 Fisher-B

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發(fā)表于 2025-3-21 18:08:11 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem
期刊簡(jiǎn)稱(chēng)with Simulations and
影響因子2023Tejas Desai
視頻videohttp://file.papertrans.cn/142/141491/141491.mp4
發(fā)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:
學(xué)科分類(lèi)SpringerBriefs in Statistics
圖書(shū)封面Titlebook: A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem; with Simulations and Tejas Desai Book 2013 The Author 2013 Fisher-B
影響因子??? ? ?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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Rural New England in Time and Placepe I errors and power of Box’s M method are presented. In the randomly-incomplete-data case, a new method is proposed. This method uses the False Discovery Rate (FDR) algorithm of Benjamini and Hochberg (J. R. Stat. Soc. Series B. ., 1289–1300, 1995). The Type I errors and power in the randomly-inco
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https://doi.org/10.1007/978-1-4614-6443-3Fisher-Behrens Problem; SAS; covariance matrices; multiple-testing; multivariate analysis; simulation
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Rural Life and Historical Archaeologyncomplete data is considered in the rest of the chapters, we thereafter clarify the idea of “missing at random (MAR)” and “missing completely at random (MCAR).” In particular, we demonstrate that if variables in a data set are all mutually dependent, then an assumption of MAR is equivalent to the assumption of MCAR.
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Rural New England in Time and Placeics and the Henze–Zirkler statistic. Type I errors and power are demonstrated using simulations in both the complete-data and the randomly-incomplete-data cases. In the randomly-incomplete-data case, we use Sidak’s method for multiple testing. Examples are also provided.
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