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Important Applications of the Behrens-Fisher Statistic and the False Discovery Rate de Tejas A. Desai

Important Applications of the Behrens-Fisher Statistic and the False Discovery Rate: SpringerBriefs in Statistics

Autor Tejas A. Desai
en Limba Engleză Paperback – 18 iun 2022
This book discusses important applications of the Behrens-Fisher statistic and the False Discovery Rate (FDR). Covered applications include ANOVA and MANOVA under potentially non-normal errors and heteroscedasticity; and an intuitive method of analyzing s x r contingency tables when the column variable is ordinal. This book also explores the novel possibility that these applications may be deemed nonparametric.

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Specificații

ISBN-13: 9783030998875
ISBN-10: 3030998878
Pagini: 83
Ilustrații: VI, 83 p.
Dimensiuni: 155 x 235 mm
Greutate: 0.14 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria SpringerBriefs in Statistics

Locul publicării:Cham, Switzerland

Cuprins

1. Introduction.- 2. An Introduction to the Bootstrap.- 3. An Introduction to the False Discovery Rate.- 4. Application to ANOVA and MANOVA.- 5. Application to Testing Equality of Several Variances and Several Covariance Matrices.- 6. Application to Testing Equality of Several Medians.- 7. Testing for Symmetry of Univariate Distributions and Central Symmetry of Multivariate Distributions.

Notă biografică

Tejas A. Desai received his PhD in Biostatistics from the University of North Carolina at Chapel Hill in 2003. He has worked at the National Institute of Environmental Health Sciences in North Carolina, at the Indian Institute of Management at Ahmedabad (IIMA), and at the Adani Institute of Infrastructure Management (AIIM). He is now a freelance consultant.

Textul de pe ultima copertă

This compact volume discusses important applications of the Behrens-Fisher statistic and the False Discovery Rate (FDR). Covered applications include ANOVA and MANOVA under potentially non-normal errors and heteroscedasticity; and an intuitive method of analyzing s x r contingency tables when the column variable is ordinal. This book also explores the novel possibility that these applications may be deemed nonparametric.