An introduction to nonparametric statistics
BackgroundOne-Sample Nonparametric InferenceTwo-Sample TestingMethods for Three or More GroupsGroup Differences with BlockingBivariate MethodsMultivariate AnalysisDensity EstimationRegression Function EstimatesResampling TechniquesAppendices. 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 …
Read More ↓Title Statements
- Full title :An introduction to nonparametric statistics / John A. Kolassa.
Summary
BackgroundOne-Sample Nonparametric InferenceTwo-Sample TestingMethods for Three or More GroupsGroup Differences with BlockingBivariate MethodsMultivariate AnalysisDensity EstimationRegression Function EstimatesResampling TechniquesAppendices.
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.
Edition Statements
- Publication Date:2021
- Edition:First edition
Publication Statements
- Publisher(s):CRC Press
- Place of Publication:Boca Raton ; London ; New York
- Language:eng
- Publication Date:2021
The Physical Object
- Description:1 Online-Ressource (xii, 212 pages) : : Illustrationen, Diagramme
Edition Identifiers
- ISBN:9780429202759, 042920275X
- DOI:10.1201/9780429202759
- oclc:on1268180267
- Dewey Decimal:519.5
Subjects
Writers
More from this Series
No reviews yet. Be the first to rate this work.
Rate & Review