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Distribution-Free Statistical Methods

Distribution-Free Statistical Methods
Author: J. S. Maritz
Publisher: Springer
Total Pages: 282
Release: 1981
Genre: Mathematics
ISBN:

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Basic concepts in distribution-free methods; One-sample location problems; Miscellaneous one-sample problems; Two-sample problems; Straight-line regression; Multiple regression and general linear models; Bivariate problems; Appendix; Bibliography.


Distribution-free Statistical Tests

Distribution-free Statistical Tests
Author: James Vandiver Bradley
Publisher: Prentice Hall
Total Pages: 410
Release: 1968
Genre: Mathematics
ISBN:

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For students, statisticians, and other research workers.


A Distribution-Free Theory of Nonparametric Regression

A Distribution-Free Theory of Nonparametric Regression
Author: László Györfi
Publisher: Springer Science & Business Media
Total Pages: 662
Release: 2006-04-18
Genre: Mathematics
ISBN: 0387224424

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This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.


Statistics from A to Z

Statistics from A to Z
Author: Andrew A. Jawlik
Publisher: John Wiley & Sons
Total Pages: 448
Release: 2016-09-21
Genre: Mathematics
ISBN: 1119272009

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Statistics is confusing, even for smart, technically competent people. And many students and professionals find that existing books and web resources don’t give them an intuitive understanding of confusing statistical concepts. That is why this book is needed. Some of the unique qualities of this book are: • Easy to Understand: Uses unique “graphics that teach” such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance “rememberability.” • Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam. • Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments. As a result, this book will be useful for business professionals and industrial engineers in addition to students and professionals in the social and physical sciences. In addition, each of the 60+ concepts is covered in one or more articles. The 75 articles in the book are usually 5–7 pages long, ensuring that things are presented in “bite-sized chunks.” The first page of each article typically lists five “Keys to Understanding” which tell the reader everything they need to know on one page. This book also contains an article on “Which Statistical Tool to Use to Solve Some Common Problems”, additional “Which to Use When” articles on Control Charts, Distributions, and Charts/Graphs/Plots, as well as articles explaining how different concepts work together (e.g., how Alpha, p, Critical Value, and Test Statistic interrelate). ANDREW A. JAWLIK received his B.S. in Mathematics and his M.S. in Mathematics and Computer Science from the University of Michigan. He held jobs with IBM in marketing, sales, finance, and information technology, as well as a position as Process Executive. In these jobs, he learned how to communicate difficult technical concepts in easy - to - understand terms. He completed Lean Six Sigma Black Belt coursework at the IASSC - accredited Pyzdek Institute. In order to understand the confusing statistics involved, he wrote explanations in his own words and graphics. Using this material, he passed the certification exam with a perfect score. Those statistical explanations then became the starting point for this book.


Distribution-Free Statistical Methods, Second Edition

Distribution-Free Statistical Methods, Second Edition
Author: J.S. Maritz
Publisher: CRC Press
Total Pages: 268
Release: 2020-11-25
Genre: Mathematics
ISBN: 1000109968

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Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research. This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown, and this section of the book has been expanded accordingly. Finally, Distribution-Free Statistical Methods includes more examples with actual data sets appearing in the text.


Bayesian Statistics for Experimental Scientists

Bayesian Statistics for Experimental Scientists
Author: Richard A. Chechile
Publisher: MIT Press
Total Pages: 473
Release: 2020-09-08
Genre: Mathematics
ISBN: 0262044587

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An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis. This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only well-developed methods for doing Bayesian statistics but also novel tools that enable Bayesian statistical analyses for cases that previously did not have a full Bayesian solution. The book's premise is that there are fundamental problems with orthodox frequentist statistical analyses that distort the scientific process. Side-by-side comparisons of Bayesian and frequentist methods illustrate the mismatch between the needs of experimental scientists in making inferences from data and the properties of the standard tools of classical statistics. The book first covers elementary probability theory, the binomial model, the multinomial model, and methods for comparing different experimental conditions or groups. It then turns its focus to distribution-free statistics that are based on having ranked data, examining data from experimental studies and rank-based correlative methods. Each chapter includes exercises that help readers achieve a more complete understanding of the material. The book devotes considerable attention not only to the linkage of statistics to practices in experimental science but also to the theoretical foundations of statistics. Frequentist statistical practices often violate their own theoretical premises. The beauty of Bayesian statistics, readers will learn, is that it is an internally coherent system of scientific inference that can be proved from probability theory.


Distribution-free Statistical Tests

Distribution-free Statistical Tests
Author: James Vandiver Bradley
Publisher:
Total Pages: 392
Release: 1960
Genre: Nonparametric statistics
ISBN:

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Distribution-free Statistics

Distribution-free Statistics
Author: Joachim Krauth
Publisher: Elsevier Publishing Company
Total Pages: 404
Release: 1988
Genre: Mathematics
ISBN:

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This is a practical non-parametric handbook of statistical tests for use in general research, but particularly useful in the fields of Behavioural and Neural Sciences. Topics covered include: a full explanation of statistical terms, correct test choice and applications, small and large sample procedures, interpretation of test results, dependence testing and independent and dependent testing of two, three or more samples. Many of these tests are based on extensive modern statistical research, so are more relevant and accurate than the earlier book on the subject (Siegel 1956). Results obtained from traditional and modern methodologies have been investigated and included for comparison of significance and variance by the reader.


Approximate Distributions of Order Statistics

Approximate Distributions of Order Statistics
Author: Rolf-Dieter Reiss
Publisher: Springer Science & Business Media
Total Pages: 363
Release: 2012-12-06
Genre: Mathematics
ISBN: 1461396204

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This book is designed as a unified and mathematically rigorous treatment of some recent developments of the asymptotic distribution theory of order statistics (including the extreme order statistics) that are relevant for statistical theory and its applications. Particular emphasis is placed on results concern ing the accuracy oflimit theorems, on higher order approximations, and other approximations in quite a general sense. Contrary to the classical limit theorems that primarily concern the weak convergence of distribution functions, our main results will be formulated in terms of the variational and the Hellinger distance. These results will form the proper springboard for the investigation of parametric approximations of nonparametric models of joint distributions of order statistics. The approxi mating models include normal as well as extreme value models. Several applications will show the usefulness of this approach. Other recent developments in statistics like nonparametric curve estima tion and the bootstrap method will be studied as far as order statistics are concerned. 1n connection with this, graphical methods will, to some extent, be explored.