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An Introduction to Nonparametric Statistics: Chapman & Hall/CRC Texts in Statistical Science

Autor John E. Kolassa
en Limba Engleză Hardback – 29 sep 2020
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.
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Specificații

ISBN-13: 9780367194840
ISBN-10: 0367194848
Pagini: 224
Ilustrații: 35 Line drawings, black and white; 22 Tables, black and white; 35 Illustrations, black and white
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.57 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science


Cuprins

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

Recenzii

'In my opinion, nonparametric tests, proposed in the book can be applied in a wide range of scientific fields, and scientists who are not familiar with mathematics but have a basic knowledge of working in R can find many useful techniques for analysing their research data.'
-Maria Ivanchuk, International Society for Clinical Biostatistics, 71, 2021

Descriere

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.