
Theory of Nonparametric Tests
Thorsten Dickhaus(Author)
Springer (Publisher)
Published on 12. January 2019
Book
Paperback/Softback
X, 124 pages
978-3-030-09462-1 (ISBN)
Description
This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.
More details
Edition
Softcover Reprint of the Original 1st 2018 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Illustrations
2 s/w Abbildungen, 1 farbige Abbildung
X, 124 p. 3 illus., 1 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 8 mm
Weight
224 gr
ISBN-13
978-3-030-09462-1 (9783030094621)
DOI
10.1007/978-3-319-76315-6
Schweitzer Classification
Other editions
Additional editions

Thorsten Dickhaus
Theory of Nonparametric Tests
Book
04/2018
Springer
€80.24
Shipment within 10-15 days
Person
Thorsten Dickhaus
obtained his Ph.D. in mathematics from the Heinrich Heine University, Düsseldorf, Germany in 2008. He held postdoc positions at the German Diabetes Center Düsseldorf and Berlin Institute of Technology. In 2010 he became a Junior Professor of Mathematical Statistics at the Humboldt University of Berlin, Germany. Since 2015 he has been a Full Professor of Mathematical Statistics at the University of Bremen, Germany.
Content
Introduction and Examples.- Empirical Measure, Empirical Processes.- Goodness-of-fit Tests.- Rank Tests.- Asymptotics of Linear Resampling Statistics.- Bootstrap Methods for Linear Models.- Projection Tests.- Some Extensions.