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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 selection and density estimation. It relies on parametric, semiparametric and nonparametric theory, which is kept at an intermediate level; the intuition 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 examples include the R-code. Because many methods described in this book belong to the basic toolbox of almost every statistician, the book should be of interest to a wide audience. In particular, the book may be useful for researchers, graduate students and PhD students who need a starting point for doing research in the area of goodness-of-fit testing. Practitioners and applied statisticians may also be interested because of the many examples, the R-code and the stress on the informative nature of the procedures.

Comparing Apples to Oranges

th This book is published to commemorate the 50 Anniversary of the S.S. Huebner Foundation for Insurance Education. Administered at the Wharton School of the University of Pennsylvania, the Huebner Foundation was established in 1941 to strengthen insurance education at the collegiate level by increasing the number of professors specializing in insurance and enriching the literature in the field. The financial support of leading life insurance companies has enabled the Foundation to provide post-graduate education for prospective insurance teachers and scholars. Through its fellowship program, the Foundation supports students in the Ph.D. program in Risk and Insurance at the Wharton School. The success of the Foundation is measured by the accomplishments of its alumni. Former Huebner Fellows play leading roles in every major area of insurance education. Fellows teach insurance to tens of thousands of undergraduate and MBA students each year and have written hundreds of books and thousands of articles on insurance. Fellows hold leadership positions at the American College, the Life Office Management Association, and the Certified Employee Benefit Specialist Program. The Foundation was created in honor of Dr. Solomon S. Huebner, a pioneer in insurance education. Dr. Huebner taught the first organized course on the economics of insurance ever offered at the collegiate level in 1904. An internationally recognized author and teacher, Dr. Huebner had a profound impact on both insurance education and the insurance industry. He served on the faculty of the Wharton School for more than nearly fifty years.

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