Randomization, bootstrap, and Monte Carlo methods in biology / Bryan F.J. Manly, Jorge A. Navarro Alberto. -- Fourth edition. -- Boca Raton, FL : CRC Press, Taylor & Francis Group, 2022. – (58.1057/M279/4th ed.)

Contents

Preface to the Fourth Edition
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Authors
1  Randomization
     1.1   The Idea of a Randomization Test
     1.2   Aspects of Randomization Testing Raised by the Example
     1.3   Confidence Limits by Randomization
     1.4   Randomization and Observational Studies
2  The Bootstrap
     2.1   Resampling with Replacement
     2.2   Standard Bootstrap Confidence Limits
     2.3   Simple Percentile Confidence Limits
     2.4   Bias-Corrected Percentile Confidence Limits
     2.5   Accelerated Bias-Corrected Percentile Limits
     2.6   Other Methods for Constructing Confidence Intervals
     2.7   Transformations to Improve Bootstrap-t Intervals
     2.8   Parametric Confidence Intervals
     2.9   A Better Estimate of Bias
     2.10   Bootstrap Tests of Significance
     2.11   Balanced Bootstrap Sampling
     2.12   Bootstrapping with Models for Count Data
3  Monte Carlo Methods
     3.1   Monte Carlo Tests
     3.2   Generalized Monte Carlo Tests
     3.3   Implicit Statistical Models
4  Some General Considerations
     4.1   Questions about Computer-Intensive Methods
     4.2   Power
     4.3   Number of Random Sets of Data Needed for a Test
     4.4   Determining a Randomization Distribution Exactly
     4.5   The Number of Replications for Confidence Intervals
     4.6   More Efficient Bootstrap Sampling Methods
     4.7   The Generation of Pseudo-Random Numbers
     4.8   The Generation of Random Permutations
5  One-and Two-Sample Tests
     5.1   The Paired Comparisons Design
   5.2   The One-Sample Randomization Test
   5.3   The Two-Sample Randomization Test
   5.4   Bootstrap Tests
   5.5   Randomizing Residuals
   5.6   Comparing the Variation in Two Samples
   5.7   A Simulation Study
     5.8   Comparison of Two Samples on Multiple Measurements.
6  Analysis of Variance
     6.1   One-Factor Analysis of Variance
   6.2   Tests for Constant Variance
   6.3   Testing for Mean Differences Using Residuals
   6.4   Examples of More Complicated Types of Analysis of Variance
   6.5   Procedures for Handling Unequal Variances
   6.6   Other Aspects of Analysis of Variance
     Exercises
7  Regression Analysis
   7.1   Simple Linear Regression
   7.2   Randomizing Residuals
   7.3   Testing for a Non-Zero g Value
   7.4   Confidence Limits for 13
   7.5   Multiple Linear Regression
   7.6   Alternative Randomization Methods with Multiple Regression
   7.7   Bootstrapping with Regression
   7.8   Further Reading
   Exercises
8  Distance Matrices and Spatial Data
   8.1   Testing for Association between Distance Matrices
   8.2   The Mantel Test
   8.3   Sampling the Randomization Distribution
   8.4   Confidence Limits for Regression Coefficients
   8.5   The Multiple Mantel Test
   8.6   Other Approaches with More Than Two Matrices
   8.7   Further Reading
   Exercises
9  Other Analyses on Spatial Data
     9.1   Spatial Data Analysis
   9.2   The Study of Spatial Point Patterns
   9.3   Mead's Randomization Test
     9.4   Tests for Randomness Based on Distances
   9.5   Testing for an Association between Two Point Patterns
   9.6   The Besag-Diggle Test
     9.7   Tests Using Distances between Points
   9.8   Testing for Random Marking
     9.9   Further Reading
     Exercises
10  Time Series
     10.1  Randomization and Time Series
     10.2  Randomization Tests for Serial Correlation
     10.3  Randomization Tests for Trend
     10.4  Randomization Tests for Periodicity
   10.5  Irregularly Spaced Series
     10.6  Tests on Times of Occurrence
     10.7  Discussion on Procedures for Irregular Series
     10.8  Bootstrap Methods
     10.9  Monte Carlo Methods
     10.10 Model-Based versus Moving Block Resampling
     10.11 Further Reading
11  Survival and Growth Data
     11.1  Bootstrapping Survival Data
     11.2  Bootstrapping for Variable Selection
     11.3  Bootstrapping for Model Selection
     11.4  Group Comparisons
     11.5  Growth Data
     11.6  Further Reading
     Exercises
12  Non-Standard Situations
    12.1  The Construction of Tests in Non-Standard Situations..
     12.2  Species Co-Occurrences on Islands
     12.3  Alternative Switching Algorithms
     12.4  Examining Time Changes in Niche Overlap
     12.5  Probing Multivariate Data with Random Skewers
     12.6  Ant Species Sizes in Europe
13  Bayesian Methods
     13.1  The Bayesian Approach to Data Analysis
    13.2  The Gibbs Sampler and Related Methods
     13.3 Biological Applications
     13.4  Further Reading
14  Conclusion and Final Comments
     14.1  Randomization
    14.2  Bootstrapping
     14.3  Monte Carlo Methods in General
     14.4  Classical versus Bayesian Inference
Appendix: Software for Computer-Intensive Statistics
References
Index