Comparing Distributions [Paperback]

Provides a self-contained comprehensive treatment of both one-sample and K-sample goodness-of-fit methods by linking them to a common theory backbone

Contains many data examples, including R-code and a specific R-package for comparing distributions

Emphesises informative statistical analysis rather than plain statistical hypothesis testing

This volume provides a theoretical treatment of a wide range of goodness-of-fit statistical methods. By comparing techniques for both one-sample and k-sample problems, the text offers a wider perspective that considers graphic and estimation parameters, and emphasizes their theoretical similarities.

Comparing Distributions refers to the statistical data analysis that encompasses the traditional goodness-of-fit testing. Whereas the latter includes only formal statistical hypothesis tests for the one-sample and the K-sample problems, this book presents a more general and informative treatment by also considering graphical and estimation methods. A procedure is said to be informative when it provides information on the reason for rejecting the null hypothesis. Despite the historically seemingly different development of methods, this book emphasises the similarities between the methods by linking them to a common theory backbone.

This book consists of two parts. In the first part statistical methods for the one-sample problem are discussed. The second part of the book treats the K-sample problem. Many sections of this second part of the book may be of interest to every statistician who is involved in comparative studies.

The book gives a self-contained theoretical treatment of a wide range of goodness-of-fit methods, including graphical methods, hypothesis tests, model selectionl3‚