An Introduction to Nonparametric Statistics

1. Auflage 2020
ISBN: 978-0-367-19484-0
Verlag: CRC Press

Buch, Englisch, 224 Seiten, Format (B × H): 155 mm x 236 mm, Gewicht: 499 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well. These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression.

Attention is paid to the intellectual development of the field, with a thorough review of bibliographical references. Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included.

Features

- Rank-based techniques including sign, Kruskal-Wallis, Friedman, Mann-Whitney and Wilcoxon tests are presented

- Tests are inverted to produce estimates and confidence intervals

- Multivariate tests are explored

- Techniques reflecting the dependence of a response variable on explanatory variables are presented

- Density estimation is explored

- The bootstrap and jackknife are discussed

This text is intended for a graduate student in applied statistics. The course is best taken after an introductory course in statistical methodology, elementary probability, and regression. Mathematical prerequisites include calculus through multivariate differentiation and integration, and, ideally, a course in matrix algebra.

Autoren/Hrsg.

Kolassa, John E

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik
• Mathematik | Informatik Mathematik Mathematik Allgemein Mengenlehre
• Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologie: Allgemeines
• Mathematik | Informatik Mathematik Algebra Lineare und multilineare Algebra, Matrizentheorie

Inhaltsverzeichnis

1. Background 2. One-Sample Nonparametric Inference 3. Two-Sample Testing 4. Methods for Three or More Groups 5. Group Differences with Blocking 6. Bivariate Methods 7. Multivariate Analysis 8. Density Estimation 9. Regression Function Estimates 10. Resampling Techniques Appendices

Über Autor(innen)

John Kolassa is Professor of Statistics and Biostatistics, Rutgers, the State University of New Jersey.