A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem

with Simulations and Examples in SAS®

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Application Software
Cover of the book A Multiple-Testing Approach to the Multivariate Behrens-Fisher Problem by Tejas Desai, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Tejas Desai ISBN: 9781461464433
Publisher: Springer New York Publication: February 26, 2013
Imprint: Springer Language: English
Author: Tejas Desai
ISBN: 9781461464433
Publisher: Springer New York
Publication: February 26, 2013
Imprint: Springer
Language: English

​​ ​    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. ​

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

​​ ​    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. ​

More books from Springer New York

Cover of the book Introduction to Solid Mechanics by Tejas Desai
Cover of the book College Students with ADHD by Tejas Desai
Cover of the book Essentials of Food Science by Tejas Desai
Cover of the book Advanced Web Services by Tejas Desai
Cover of the book Development of an Environmental and Economic Assessment Tool (Enveco Tool) for Fire Events by Tejas Desai
Cover of the book Fetal Stem Cells in Regenerative Medicine by Tejas Desai
Cover of the book Residue Reviews by Tejas Desai
Cover of the book Multiscale Signal Analysis and Modeling by Tejas Desai
Cover of the book Robust Output LQ Optimal Control via Integral Sliding Modes by Tejas Desai
Cover of the book Essential Medical Facts Every Clinician Should Know by Tejas Desai
Cover of the book Getting the Most out of Your Mentoring Relationships by Tejas Desai
Cover of the book Biogeochemistry of a Forested Ecosystem by Tejas Desai
Cover of the book Shoulder Arthroscopy by Tejas Desai
Cover of the book The Physical Basis of Bacterial Quorum Communication by Tejas Desai
Cover of the book Electron Lenses for Super-Colliders by Tejas Desai
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy