ALBERT

Note: 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 β Value. - 8.4 Confidence Limits for β. - 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.