Randomization, Bootstrap and Monte Carlo Methods in Biology  book cover
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3rd Edition

Randomization, Bootstrap and Monte Carlo Methods in Biology




ISBN 9781584885412
Published August 15, 2006 by Chapman and Hall/CRC
480 Pages - 33 B/W Illustrations

 
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Book 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

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