Randomization, Bootstrap and Monte Carlo Methods in Biology

Bryan F.J. Manly, Jorge A. Navarro Alberto

• Éditeur: Chapman and Hall/CRC
• Année: 2020
• ISBN:
  9781000080544
• Édition: 4
• Sujet: Mathematics
• Sous-sujet: Probability & Statistics

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online.

Features

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• Presents an overview of computer-intensive statistical methods and applications in biology

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• Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods

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• Makes it easy for biologists, researchers, and students to understand the methods used

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• Provides information about computer programs and packages to implement calculations, particularly using R code

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• Includes a large number of real examples from a range of biological disciplines

Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.

Table des matières

• Cover
• Half-Title
• Series
• Title
• Copyright
• Dedication
• Contents
• Preface to the Fourth Edition
• Preface to the Third Edition
• Preface to the Second Edition
• Preface to the First Edition
• Authors
• 1 Randomization
• 2 The Bootstrap
• 3 Monte Carlo Methods
• 4 Some General Considerations
• 5 One- and Two-Sample Tests
• 6 Analysis of Variance
• 7 Regression Analysis
• 8 Distance Matrices and Spatial Data
• 9 Other Analyses on Spatial Data
• 10 Time Series
• 11 Survival and Growth Data
• 12 Non-Standard Situations
• 13 Bayesian Methods
• 14 Conclusion and Final Comments
• Appendix: Software for Computer-Intensive Statistics
• References
• Index