Click to have a closer look
About this book
About this book
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.
RANDOMIZATIONThe Idea of a Randomization TestExamples of Randomization TestsAspects of Randomization Testing Raised by the ExamplesConfidence Limits by RandomizationApplications of Randomization in Biology and Related AreasRandomization and Observational StudiesChapter SummaryTHE JACKKNIFEThe Jackknife EstimatorApplications of Jackknifing in BiologyChapter SummaryTHE BOOTSTRAPResampling with ReplacementStandard Bootstrap Confidence LimitsSimple Percentile Confidence LimitsBias-Corrected Percentile Confidence LimitsAccelerated Bias-Corrected Percentile LimitsOther Methods for Constructing Confidence IntervalsTransformations to Improve Bootstrap-t IntervalsParametric Confidence IntervalsA Better Estimate of BiasBootstrap Tests of SignificanceBalanced Bootstrap SamplingApplications of Bootstrapping in BiologyFurther ReadingChapter SummaryMONTE CARLO METHODSMonte Carlo TestsGeneralized Monte Carlo TestsImplicit Statistical ModelsApplications of Monte Carlo Methods in BiologyChapter SummarySOME GENERAL CONSIDERATIONSQuestions about Computer-Intensive MethodsPowerNumber of Random Sets of Data Needed for a TestDetermining a Randomization Distribution ExactlyThe Number of Replications for Confidence IntervalsMore Efficient Bootstrap Sampling MethodsThe Generation of Pseudo-Random NumbersThe Generation of Random PermutationsChapter SummaryONE- AND TWO-SAMPLE TESTSThe Paired Comparisons DesignThe One-Sample Randomization TestThe Two-Sample Randomization TestBootstrap TestsRandomizing ResidualsComparing the Variation in Two SamplesA Simulation StudyThe Comparison of Two Samples on Multiple MeasurementsFurther ReadingChapter SummaryANALYSIS OF VARIANCEOne-Factor Analysis of VarianceTests for Constant VarianceTesting for Mean Differences Using ResidualsExamples of More Complicated Types of Analysis of VarianceProcedures for Handling Unequal VariancesOther Aspects of Analysis of VarianceFurther ReadingChapter SummaryREGRESSION ANALYSISSimple Linear RegressionRandomizing ResidualsTesting for a Nonzero ValueConfidence Limits for Multiple Linear RegressionAlternative Randomization Methods with Multiple RegressionBootstrapping and Jackknifing with RegressionFurther ReadingChapter SummaryDISTANCE MATRICES AND SPATIAL DATATesting for Association between Distance MatricesThe Mantel TestSampling the Randomization DistributionConfidence Limits for Regression CoefficientsThe Multiple Mantel TestOther Approaches with More Than Two MatricesFurther ReadingChapter SummaryOTHER ANALYSES ON SPATIAL DATASpatial Data AnalysisThe Study of Spatial Point PatternsMead's Randomization TestTests for Randomness Based on DistancesTesting for an Association between Two Point PatternsThe Besag-Diggle TestTests Using Distances Between PointsTesting for Random MarkingFurther ReadingChapter S