Tejas Desai
A Multiple-testing Approach to the Multivariate Behrens-fisher Problem: with Simulations and Examples in Sas - Springerbriefs in Statistics Tejas Desai 2013 edition

Name: A Multiple-testing Approach to the Multivariate Behrens-fisher Problem: with Simulations and Examples in Sas - Springerbriefs in Statistics
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A Multiple-testing Approach to the Multivariate Behrens-fisher Problem: with Simulations and Examples in Sas - Springerbriefs in Statistics

Tejas Desai

2013 edition

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 as the complete-data case. Moreover, all methods considered in this monograph will be tested using both simulations and examples.

55 pages, 5 black & white tables, biography

メディア	書籍 Paperback Book (ソフトカバーで背表紙を接着した本)
リリース済み	2013年2月23日
ISBN13	9781461464426
出版社	Springer-Verlag New York Inc.
ページ数	55
寸法	156 × 234 × 3 mm · 104 g
言語	英語