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

✍ Scribed by Bryan F.J. Manly

• Publisher: Chapman and Hall/CRC
• Year: 2006
• Tongue: English
• Leaves: 478
• Edition: 3
• Category: Library

✦ Synopsis

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications.

This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals.

New to the Third Edition

Updated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statistics

References that reflect recent developments in methodology and computing techniques

Additional references on new applications of computer-intensive methods in biology

Providing comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology.

✦ Table of Contents

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