Randomization, bootstrap and Monte Carlo methods in biology
Author(s)
Bibliographic Information
Randomization, bootstrap and Monte Carlo methods in biology
（Texts in statistical science）
CRC Press, 2020
4th ed. / Bryan F.J. Manly, Jorge A. Navarro Alberto
- : hbk
Available at / 4 libraries
-
No Libraries matched.
- Remove all filters.
Note
Previous ed.: published as by Bryan F.J. Manly
Bibliography: p. 299-330
Includes index
"A Chapman & Hall book"
Description and Table of Contents
Description
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
Presents an overview of computer-intensive statistical methods and applications in biology
Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods
Makes it easy for biologists, researchers, and students to understand the methods used
Provides information about computer programs and packages to implement calculations, particularly using R code
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 of Contents
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
15.Appendix: Software for Computer-Intensive Statistics
by "Nielsen BookData"