Last edited by Faekree
Tuesday, October 20, 2020 | History

8 edition of Resampling methods found in the catalog.

Resampling methods

a practical guide to data analysis

by Phillip I. Good

  • 138 Want to read
  • 26 Currently reading

Published by Birkhäuser in Boston .
Written in English

    Subjects:
  • Resampling (Statistics)

  • Edition Notes

    Includes bibliographical references (p. [247]-264) and index.

    StatementPhillip I. Good.
    Classifications
    LC ClassificationsQA278.8 .G66 1999
    The Physical Object
    Paginationxii, 269 p. :
    Number of Pages269
    ID Numbers
    Open LibraryOL366516M
    ISBN 100817640916, 3764340916
    LC Control Number98026978

    This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Rating: (not yet rated) 0 with reviews - Be the first. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics.

    Resampling Methods. Resampling methods (see, e.g., Efron, ) draw samples from the observed data to draw certain conclusions about the population of interest. Two of the most popular resampling methods are the jackknife and bootstrap. Both of these are examples of . More examples of Randomization and Bootstrap methods (Simon, ): Simon produced a book “Resampling: the New Statistics”, an example based book on Monte Carlo, Permutation (Randomization) tests, and Bootstrap available for free on the Resampling Stats website. I found the following examples demonstrate the effectiveness of these methods.

    Sample-rate conversion is the process of changing the sampling rate of a discrete signal to obtain a new discrete representation of the underlying continuous signal. Application areas include image scaling and audio/visual systems, where different sampling rates may be used for engineering, economic, or historical reasons.. For example, Compact Disc Digital Audio and Digital Audio Tape systems. Resampling (drawing repeated samples from the given data, or population suggested by the data) is a proven cure. Bootstrap, permutation, and other computer-intensive procedures have revolutionized statistics. Resampling is now the method of choice for confidence limits, hypothesis tests, and other everyday inferential problems.


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Resampling methods by Phillip I. Good Download PDF EPUB FB2

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods.

It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and by: With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods.

It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.

Resampling Methods: A Practical Guide to Data Analysis - Kindle edition by Good, Phillip I. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and Resampling methods book while reading Resampling Methods: A Practical Guide to Data Analysis/5(4).

"This book is devoted to resampling methods fordependent data, which has been a fast developing area in about the last twenty years. a ] provides an introduction to the area of resampling methods for dependent data and also presents the latest results in the area with quite a long reference by: "Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis.

The author attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it. Resampling Methods: A Practical Guide to Data Analysis.

Second Edition - Phillip I. Good - Google Books "Most introductory statistics books ignore or give little attention to resampling methods, 3/5(2). Resampling: The New Statistics contains a number of examples in Resampling Stats, a computer program originated by Simon, but can be read on its own without the program.

Covers probability, hypothesis testing, confidence intervals and sample size calculations and Cited by: Introduction. Resampling methods are a natural extension of simulation.1 The analyst uses a computer to generate a large number of simulated samples, then analyzes and summarizes patterns in those samples.

The key difference is that the analyst begins with the observed data instead of a theoretical probability distribution. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods.

It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences /5(2).

This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence. Like the resam­ pling methods for independent data, these methods provide tools for sta­ tistical analysis of dependent data without requiring stringent structural assumptions.

Resampling Methods for Dependent Data. This is a book on bootstrap and related resampling methods for temporal and spatial data exhibiting various forms of dependence.

Like the resam pling methods for independent data, these methods provide tools for sta tistical analysis of dependent data without requiring stringent structural assumptions. Their book is destined to be used widely in graduate social science statistics classes around the world.

Monte Carlo simulation and resampling are the workhorse of modern methods. Carsey and Harden provide the perfect, accessible guide to learn this fundamental, must-have skill for social scientists/5(7).

Resampling Resampling strategies are usually used to assess the performance of a learning algorithm. mlr3 entails 6 predefined resampling strategies: Cross-validation, Leave-one-out cross validation, Repeated cross-validation, Out-of-bag bootstrap and other variants (e.g.

b), Monte-Carlo cross-validation and Holdout. that resampling procedures will soon supplant common nonparametric procedures and may displace most parametric procedures as well.

This paper introduces the vocabulary, logic, and demonstrates basic applications of permutation and bootstrap resampling methods. Resampling methods have become practical with the general availability of cheap rapidFile Size: KB.

Resampling Methods: a Practical Guide to Data Analysis. [Phillip I Good] -- "Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Immediate download free Resampling Methods: A Practical Guide to Data Analysis book by clicking the link above.

Excellent testimonies have actually been given for the Resampling Methods: A Practical Guide to Data Analysis book.

This book is truly good as. Resampling Methods: A Practical Guide to Data Analysis, Edition 2 - Ebook written by Phillip I.

Good. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Resampling Methods: A Practical Guide to Data Analysis, Edition /5(1). The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation.

Complete R code from all examples is provided so readers can replicate every analysis presented using R. Resampling Methods: A Practical Guide to Data Analysis eBook: Good, Phillip I.: : Kindle Store5/5(1). If you need to learn about resampling, this book would be a good place to start."--Technometrics (Review of Second Edition) This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests.

With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests.

Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods.Resampling Methods: A Practical Guide to Data Analysis, Edition 3 - Ebook written by Phillip I.

Good. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Resampling Methods: A Practical Guide to Data Analysis, Edition 3.