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Author: Peter Sprent
ISBN13: 978-0412306105
Title: Applied Nonparametric Statistical Methods (Chapman & Hall Statistics Text Series)
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Language: English
Category: Mathematics
Publisher: Chapman & Hall; 1989 ed. edition (February 1, 1989)
Pages: 220

Applied Nonparametric Statistical Methods (Chapman & Hall Statistics Text Series) by Peter Sprent

Applied Nonparametric Statistical Methods is an exception. The book’s major strength is its prioritization of coverage. It is also a good textbook for undergraduate courses in statistics as well as courses for students majoring in other disciplines. Applied Nonparametric Statistical Methods provides a very clear exposition of modern nonparametric methods.

Recent studies show that semiparametric methods and models may be applied to solve dimensionality reduction problems arising from using fully nonparametric models and methods. Answering the call for an up-to-date overview of the latest developments in the field, Nonlinear Time Series: Semiparametric and Nonparametric Methods focuses on various semiparametric methods in model estimation, specification testing, and selection of time series data. Monographs on Statistics and Applied Probability 108. Nonlinear Time Series Semiparametric and Nonparametric Methods.

Applied nonparametric statistical methods. 3rd ed. I P Sprent, .

Nonlinear Time Series: Semiparametric and Nonparametric Methods (Chapman & Hall CRC Monographs on Statistics & Applied Probability). Statistics: Principles and Methods. Expansions and Asymptotics for Statistics C5904 FM Statistics in the 21st Century (Chapman & Hall CRC Monographs on Statistics & Applied Probability). Principles of Applied Statistics

This resource covers key methods in time series analysis and provides the necessary theoretical details. Скачать (pdf, . 9 Mb) Читать. Epub FB2 mobi txt RTF. Конвертация файла может нарушить форматирование оригинала. По-возможности скачивайте файл в оригинальном формате.

Alan J. B. Anderson, Chapman and Hall, London, 1989. No. of pages: xvi + 223. Price: E1. 5 Many elementary statistics texts leave students with the false impression that the job of the statis- tician is mainly to analyse data.

This resource covers key methods in time series analysis and provides the necessary theoretical details. 4 Mb) Читать.

Chapman & Hall/CRC Monographs on Statistics & Applied Probability. It will certainly become a standard reference for nonparametric and robust methods.

April 24, 2010 History found in the catalog. Applied Non-Parametric Statistical Methods (Chapman & Hall Statistics. 1 2 3 4 5. Want to Read. Are you sure you want to remove Applied Non-Parametric Statistical Methods (Chapman & Hall Statistics Text Series) from your list? Applied Non-Parametric Statistical Methods (Chapman & Hall Statistics. One need know only a little statistics to make sensible use of simple nonparametric methods. OTHER STATISTICS TEXTS FROM CHAPMAN AND HALL The Analysis of Time Series C. Chatfield Statistics for Technology C. Chatfield. Introduction to Multivariate Analysis C. Chatfield and A. 1. Collins. Applied Statistics D. R. Cox and E. Snell. An Introduction to Statistical Modelling . Sprent, Peter Applied nonparametric statistical methods. Nonparametric statistical mathematics. Applications I. Title 51.

This new edition of Peter Sprent's highly successful introduction to nonparametric methods retains many features acclaimed by reviewers of the first edition. Basics are simply and carefully explained using many realistic but simple examples. The text has been completely revised to highlight the way modern computer software enables us to carry out exact tests based on permutation distributions where one had previously to resort to asymptotic results in situations where these are now known to be misleading. New methods are introduced in this edition and relationships between inference procedures that may at first sight appear to be unrelated are clearly demonstrated. More attention is given to the analysis of censored data and the sections on analysis of counts and categorical data have been greatly extended. This additional material is relevant to applications in fields as diverse as medicine, the social sciences, industry and market research. This book should be of interest to research workers in industry, pharmaceutical firms, and all who use basic statistical methods. It should also be a useful text for undergraduate students taking university and polytechnic courses.
Reviews: 3
I haven't read the whole book, but I have other books by Peter Sprent, and they are hard to beat. He has a nice writing style, and gives great examples. You won't be disappointed.
Nearly all of traditional statistical tests assume that data points are distributed along the "normal," Gaussian bell curve. The assumption may be explicit, may be hidden inside a chi-squared phrase, or may stand silently behind discussions of mean and variance. If your data don't match that assumption, stated or not, the tests give wrong answers.

Nonparametric stats work without that assumption. In fact, most work without !any! assumptions about the distributions of data. These are very robust techniques, and this book demonstrates a number of simple and effective ones. The author describes everything you need to know about a number of tests: when each applies, how to perform it, and how to interpret results. It's a very useful guide for people who need high-quality answers from low-quality data.

Best, many of these procedures are simple enough to apply routinely to almost any data set. I find them helpful for quick checks before applying more detailed kinds of parametric analysis. If your data fail the loose bounds of non-parametric testing, you know that fussier, more high-strung tests have no hope of reasonable answers.

There are no theorems here, and very little development of the underlying principles. That's good for someone who just wants the answers. On the other hand, it's a real weakness if you need to customize analysis for an unusual problem. You just won't find the fundamentals you need for sound improvisation.


(This review describes the first edition.)
This lame excuse for a textbook was required for my class, but never used. Everyone sold theirs back at the bookstore after the semester. This textbook lacks important items such as an appendix and a lack of detailed analysis of problems. Often, it felt that the book would give questions that it did not explain how to answer in the examples. Not to mention, the definitions are not precise at all. In fact, I could not find a solid definition of "nonparametric" in this book. The only reason why I still have this book is because the bookstore would only give me $2 for it. Looking back now after I got my math degree, I am forced to tap the financial account for a nicer, more descriptive book that is more user-friendly for quicker references. Don't buy this unless it is required for a class. In which case, drop the class.