Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Texts in Statistical Science)

Cover
Half Title
Title Page
Copyright Page
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Table of Contents
Chapter 1: Randomization
	1.1 The Idea of a Randomization Test
	1.2 Examples of Randomization Tests
	1.3 Aspects of Randomization Testing Raised by the Examples
		1.3.1 Sampling the Randomization Distribution or Systematic Enumeration
		1.3.2 Equivalent Test Statistics
		1.3.3 Significance Levels for Classical and Randomization Tests
		1.3.4 Limitations of Randomization Tests
	1.4 Confidence Limits by Randomization
	1.5 Applications of Randomization in Biology and Related Areas
		1.5.1 Single Species Ecology
		1.5.2 Genetics, Evolution, and Natural Selection
		1.5.3 Community Ecology
		1.5.4 Other Environmental Applications
	1.6 Randomization and Observational Studies
	1.7 Chapter Summary
Chapter 2: The Jackknife
	2.1 The Jackknife Estimator
	2.2 Applications of Jackknifing in Biology
		2.2.1 Single-Species Analyses
		2.2.2 Genetics, Evolution, and Natural Selection
		2.2.3 Community Ecology
	2.3 Chapter Summary
Chapter 3: The Bootstrap
	3.1 Resampling with Replacement
	3.2 Standard Bootstrap Confidence Limits
	3.3 Simple Percentile Confidence Limits
	3.4 Bias-Corrected Percentile Confidence Limits
	3.5 Accelerated Bias-Corrected Percentile Limits
	3.6 Other Methods for Constructing Confidence Intervals
	3.7 Transformations to Improve Bootstrap-t Intervals
	3.8 Parametric Confidence Intervals
	3.9 A Better Estimate of Bias
	3.10 Bootstrap Tests of Significance
	3.11 Balanced Bootstrap Sampling
	3.12 Applications of Bootstrapping in Biology
		3.12.1 Single-Species Ecology
		3.12.2 Genetics, Evolution, and Natural Selection
		3.12.3 Community Ecology
		3.12.4 Other Ecological and Environmental Applications
	3.13 Further Reading
	3.14 Chapter Summary
Chapter 4: Monte Carlo Methods
	4.1 Monte Carlo Tests
	4.2 Generalized Monte Carlo Tests
	4.3 Implicit Statistical Models
	4.4 Applications of Monte Carlo Methods in Biology
		4.4.1 Single-Species Ecology
		4.4.2 Genetics and Evolution
		4.4.3 Community Ecology
	4.5 Chapter Summary
Chapter 5: Some General Considerations
	5.1 Questions about Computer-Intensive Methods
	5.2 Power
	5.3 Number of Random Sets of Data Needed for a Test
	5.4 Determining a Randomization Distribution Exactly
	5.5 The Number of Replications for Confidence Intervals
	5.6 More Efficient Bootstrap Sampling Methods
	5.7 The Generation of Pseudo-Random Numbers
	5.8 The Generation of Random Permutations
	5.9 Chapter Summary
Chapter 6: One- and Two-Sample Tests
	6.1 The Paired Comparisons Design
	6.2 The One-Sample Randomization Test
	6.3 The Two-Sample Randomization Test
	6.4 Bootstrap Tests
	6.5 Randomizing Residuals
	6.6 Comparing the Variation in Two Samples
	6.7 A Simulation Study
	6.8 The Comparison of Two Samples on Multiple Measurements
	6.9 Further Reading
	6.10 Chapter Summary
Chapter 7: Analysis of Variance
	7.1 One-Factor Analysis of Variance
	7.2 Tests for Constant Variance
	7.3 Testing for Mean Differences Using Residuals
	7.4 Examples of More Complicated Types of Analysis of Variance
	7.5 Procedures for Handling Unequal Variances
	7.6 Other Aspects of Analysis of Variance
	7.7 Further Reading
	7.8 Chapter Summary
Chapter 8: Regression Analysis
	8.1 Simple Linear Regression
	8.2 Randomizing Residuals
	8.3 Testing for a Nonzero ß Value
	8.4 Confidence Limits for ß
	8.5 Multiple Linear Regression
	8.6 Alternative Randomization Methods with Multiple Regression
	8.7 Bootstrapping and Jackknifing with Regression
	8.8 Further Reading
	8.9 Chapter Summary
Chapter 9: Distance Matrices and Spatial Data
	9.1 Testing for Association between Distance Matrices
	9.2 The Mantel Test
	9.3 Sampling the Randomization Distribution
	9.4 Confidence Limits for Regression Coefficients
	9.5 The Multiple Mantel Test
	9.6 Other Approaches with More Than Two Matrices
	9.7 Further Reading
	9.8 Chapter Summary
Chapter 10: Other Analyses on Spatial Data
	10.1 Spatial Data Analysis
	10.2 The Study of Spatial Point Patterns
	10.3 Mead’s Randomization Test
	10.4 Tests for Randomness Based on Distances
	10.5 Testing for an Association between Two Point Patterns
	10.6 The Besag–Diggle Test
	10.7 Tests Using Distances between Points
	10.8 Testing for Random Marking
	10.9 Further Reading
	10.10 Chapter Summary
Chapter 11: Time Series
	11.1 Randomization and Time Series
	11.2 Randomization Tests for Serial Correlation
	11.3 Randomization Tests for Trend
	11.4 Randomization Tests for Periodicity
	11.5 Irregularly Spaced Series
	11.6 Tests on Times of Occurrence
	11.7 Discussion on Procedures for Irregular Series
	11.8 Bootstrap Methods
	11.9 Monte Carlo Methods
	11.10 Model-Based vs. Moving-Block Resampling
	11.11 Further Reading
	11.12 Chapter Summary
Chapter 12: Multivariate Data
	12.1 Univariate and Multivariate Tests
	12.2 Sample Mean Vectors and Covariance Matrices
	12.3 Comparison of Sample Mean Vectors
	12.4 Chi-Squared Analyses for Count Data
	12.5 Comparison of Variations for Several Samples
	12.6 Principal Components Analysis and Other One-Sample Methods
	12.7 Discriminant Function Analysis
	12.8 Further Reading
	12.9 Chapter Summary
Chapter 13: Survival and Growth Data
	13.1 Bootstrapping Survival Data
	13.2 Bootstrapping for Variable Selection
	13.3 Bootstrapping for Model Selection
	13.4 Group Comparisons
	13.5 Growth Data
	13.6 Further Reading
	13.7 Chapter Summary
Chapter 14: Nonstandard Situations
	14.1 The Construction of Tests in Nonstandard Situations
	14.2 Species Co-Occurrences on Islands
	14.3 Alternative Switching Algorithms
	14.4 Examining Time Changes in Niche Overlap
	14.5 Probing Multivariate Data with Random Skewers
	14.6 Ant Species Sizes in Europe
	14.7 Chapter Summary
Chapter 15: Bayesian Methods
	15.1 The Bayesian Approach to Data Analysis
	15.2 The Gibbs Sampler and Related Methods
	15.3 Biological Applications
	15.4 Further Reading
	15.5 Chapter Summary
Chapter 16: Final Comments
	16.1 Randomization
	16.2 Bootstrapping
	16.3 Monte Carlo Methods in General
	16.4 Classical vs. Bayesian Inference
References
Appendix Software for Computer-Intensive Statistics
Author Index
Subject Index