Randomization, Bootstrap and Monte Carlo Methods in Biology: 3rd Edition (Hardback) book cover

Randomization, Bootstrap and Monte Carlo Methods in Biology

3rd Edition

By Bryan F.J. Manly

Chapman and Hall/CRC

480 pages | 33 B/W Illus.

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Hardback: 9781584885412
pub: 2006-08-15
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pub: 2018-10-03
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Description

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

    RANDOMIZATION

    The Idea of a Randomization Test

    Examples of Randomization Tests

    Aspects of Randomization Testing Raised by the Examples

    Confidence Limits by Randomization

    Applications of Randomization in Biology and Related Areas

    Randomization and Observational Studies

    Chapter Summary

    THE JACKKNIFE

    The Jackknife Estimator

    Applications of Jackknifing in Biology

    Chapter Summary

    THE BOOTSTRAP

    Resampling with Replacement

    Standard Bootstrap Confidence Limits

    Simple Percentile Confidence Limits

    Bias-Corrected Percentile Confidence Limits

    Accelerated Bias-Corrected Percentile Limits

    Other Methods for Constructing Confidence Intervals

    Transformations to Improve Bootstrap-t Intervals

    Parametric Confidence Intervals

    A Better Estimate of Bias

    Bootstrap Tests of Significance

    Balanced Bootstrap Sampling

    Applications of Bootstrapping in Biology

    Further Reading

    Chapter Summary

    MONTE CARLO METHODS

    Monte Carlo Tests

    Generalized Monte Carlo Tests

    Implicit Statistical Models

    Applications of Monte Carlo Methods in Biology

    Chapter Summary

    SOME GENERAL CONSIDERATIONS

    Questions about Computer-Intensive Methods

    Power

    Number of Random Sets of Data Needed for a Test

    Determining a Randomization Distribution Exactly

    The Number of Replications for Confidence Intervals

    More Efficient Bootstrap Sampling Methods

    The Generation of Pseudo-Random Numbers

    The Generation of Random Permutations

    Chapter Summary

    ONE- AND TWO-SAMPLE TESTS

    The Paired Comparisons Design

    The One-Sample Randomization Test

    The Two-Sample Randomization Test

    Bootstrap Tests

    Randomizing Residuals

    Comparing the Variation in Two Samples

    A Simulation Study

    The Comparison of Two Samples on Multiple Measurements

    Further Reading

    Chapter Summary

    ANALYSIS OF VARIANCE

    One-Factor Analysis of Variance

    Tests for Constant Variance

    Testing for Mean Differences Using Residuals

    Examples of More Complicated Types of Analysis of Variance

    Procedures for Handling Unequal Variances

    Other Aspects of Analysis of Variance

    Further Reading

    Chapter Summary

    REGRESSION ANALYSIS

    Simple Linear Regression

    Randomizing Residuals

    Testing for a Nonzero ß Value

    Confidence Limits for ß

    Multiple Linear Regression

    Alternative Randomization Methods with Multiple Regression

    Bootstrapping and Jackknifing with Regression

    Further Reading

    Chapter Summary

    DISTANCE MATRICES AND SPATIAL DATA

    Testing for Association between Distance Matrices

    The Mantel Test

    Sampling the Randomization Distribution

    Confidence Limits for Regression Coefficients

    The Multiple Mantel Test

    Other Approaches with More Than Two Matrices

    Further Reading

    Chapter Summary

    OTHER ANALYSES ON SPATIAL DATA

    Spatial Data Analysis

    The Study of Spatial Point Patterns

    Mead's Randomization Test

    Tests for Randomness Based on Distances

    Testing for an Association between Two Point Patterns

    The Besag-Diggle Test

    Tests Using Distances Between Points

    Testing for Random Marking

    Further Reading

    Chapter Summary

    TIME SERIES

    Randomization and Time Series

    Randomization Tests for Serial Correlation

    Randomization Tests for Trend

    Randomization Tests for Periodicity

    Irregularly Spaced Series

    Tests on Times of Occurrence

    Discussion on Procedures for Irregular Series

    Bootstrap Methods

    Monte Carlo Methods

    Model-Based vs. Moving-Block Resampling

    Further Reading

    Chapter Summary

    MULTIVARIATE DATA

    Univariate and Multivariate Tests

    Sample Mean Vectors and Covariance Matrices

    Comparison of Sample Mean Vectors

    Chi-Squared Analyses for Count Data

    Comparison of Variations for Several Samples

    Principal Components Analysis and Other

    One-Sample Methods

    Discriminant Function Analysis

    Further Reading

    Chapter Summary

    SURVIVAL AND GROWTH DATA

    Bootstrapping Survival Data

    Bootstrapping for Variable Selection

    Bootstrapping for Model Selection

    Group Comparisons

    Growth Data

    Further Reading

    Chapter Summary

    NONSTANDARD SITUATIONS

    The Construction of Tests in Nonstandard Situations

    Species Co-Occurrences on Islands

    Alternative Switching Algorithms

    Examining Time Changes in Niche Overlap

    Probing Multivariate Data with Random Skewers

    Ant Species Sizes in Europe

    Chapter Summary

    BAYESIAN METHODS

    The Bayesian Approach to Data Analysis

    The Gibbs Sampler and Related Methods

    Biological Applications

    Further Reading

    Chapter Summary

    FINAL COMMENTS

    Randomization

    Bootstrapping

    Monte Carlo Methods in General

    Classical vs. Bayesian Inference

    REFERENCES

    APPENDIX: SOFTWARE FOR COMPUTER-INTENSIVE STATISTICS

    INDEX

    Subject Categories

    BISAC Subject Codes/Headings:
    MAT029000
    MATHEMATICS / Probability & Statistics / General