Randomization, Bootstrap and Monte Carlo Methods in Biology

Randomization, Bootstrap and Monte Carlo Methods in Biology

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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.show more

Product details

  • Hardback | 480 pages
  • 158 x 234 x 30mm | 798.34g
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 3rd Revised edition
  • 33 black & white illustrations, 82 black & white tables
  • 1584885416
  • 9781584885412
  • 985,448

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 INDEXshow more

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