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Theory of Nonparametric Tests

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Theory of Nonparametric Tests/ by Thorsten Dickhaus.
Author:	Dickhaus, Thorsten.
Description:	X, 124 p. 3 illus., 1 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-319-76315-6
ISBN:	9783319763156

Theory of Nonparametric Tests
Dickhaus, Thorsten.

Theory of Nonparametric Tests[electronic resource] /by Thorsten Dickhaus. - 1st ed. 2018. - X, 124 p. 3 illus., 1 illus. in color.online resource.

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.

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.

ISBN: 9783319763156

Standard No.: 10.1007/978-3-319-76315-6doiSubjects--Topical Terms:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Theory of Nonparametric Tests
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520 $a 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.
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