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More About This Textbook
Overview
Comparing Distribution to the statistical data analysis that encompases the traditional goodness-of fit testing. Where as the latter include 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 emphasizes. Despire the historically seemingly different development of methods, this book emphasizes 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 selection and density estimation. It relies on parametric, semiparamentric and nonparametric theory, which is kept at an intermediate level; the institution and heuristics behind the methods are usually provided as well the book contains many data examples that are analysed with the cd R-package that is written by the author. All example include the R-Code.
Because many methods described in this book belong to the basic toolbox of almost every statistical, the book should be of interest to a wide audience. In particular, thebook may be useful for researchers, graduate students and PhD studets who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied and the stasticians may also be interested because of the many examples, the R-code and stress on the informative nature of the procedures.
Editorial Reviews
From the Publisher
From the reviews:“Comparing distributions means mainly goodness-of-fit testing. … A lot of details are cited from the huge reference list, which I like very much. … At the end of the sections some practical guidelines are given which are very helpful. The monograph is of interest for applied statisticians as well as for research mathematicians. It contains a good overview of tests and methods. It can be recommended for advanced seminars on testing. … I can warmly recommend it.” (Arnold Janssen, Mathematical Reviews, Issue 2010 k)
“This outstanding book is about goodness of fit (GOF) testing … . In addition the book presents some graphical tools for comparing distributions with its focus on graphs that are closely related to statistical tests. … The book contains some theory with numerous examples that are useful for both applied statisticians and nonstatistician practitioners. … In conclusion this book provides considerable information for a wide range of Technometrics readers and it is a valuable contribution to our profession.” (Subir Ghosh, Technometrics, Vol. 53 (1), February, 2011)
Product Details
Table of Contents
Part I One-sample problems Introduction.- Preliminaries (building blocks).- Graphic tools.- Smooth tests.- Methods based on the empirical distribution function.- Part II Two-sample and K-sample problems Introduction.- Preliminaries (building blocks).- Graphical tools.- Some important two-sample tests.- Smooth tests.- Methods based on the empirical distribution function.- Two final methods and some final thoughts.