Introduction to Nonparametric Statistics

List of contents

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

About the author

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

Summary

This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.

Product details

• Authors: John E. Kolassa, Kolassa John E.
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 29.09.2020
• EAN: 9780367194840
• ISBN: 978-0-367-19484-0
• No. of pages: 224
• Series: Chapman & Hall/CRC Texts in Statistical Science
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; REFERENCE / General, MATHEMATICS / Probability & Statistics / General, MATHEMATICS / Algebra / Linear, MATHEMATICS / Set Theory, Probability & statistics, Probability and statistics, PSYCHOLOGY / Essays