Verwandte Artikel zu An Introduction to Nonparametric Statistics (Chapman...

An Introduction to Nonparametric Statistics (Chapman & Hall/Crc Texts in Statistical Science) - Hardcover

Kolassa, John E.

 
9780367194840: An Introduction to Nonparametric Statistics (Chapman & Hall/Crc Texts in Statistical Science)

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

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

Von der hinteren Coverseite

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.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.