Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition

Front Cover
CRC Press, Aug 15, 2006 - Mathematics - 480 pages
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.
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    Selected pages

    Contents

    Randomization
    		7
    12 Examples of Randomization Tests
    		7
    13 Aspects of Randomization Testing Raised by the Examples
    		14
    14 Confidence Limits by Randomization
    		18
    15 Applications of Randomization in Biology and Related Areas
    		21
    16 Randomization and Observational Studies
    		25
    17 Chapter Summary
    		26
    The Jackknife
    		29
    92 The Mantel Test
    		205
    93 Sampling the Randomization Distribution
    		207
    94 Confidence Limits for Regression Coefficients
    		210
    95 The Multiple Mantel Test
    		212
    96 Other Approaches with More Than Two Matrices
    		213
    97 Further Reading
    		230
    98 Chapter Summary
    		232
    Other Analyses on Spatial Data
    		239

    22 Applications of Jackknifing in Biology
    		35
    23 Chapter Summary
    		40
    The Bootstrap
    		41
    32 Standard Bootstrap Confidence Limits
    		42
    33 Simple Percentile Confidence Limits
    		46
    34 BiasCorrected Percentile Confidence Limits
    		52
    35 Accelerated BiasCorrected Percentile Limits
    		57
    36 Other Methods for Constructing Confidence Intervals
    		65
    37 Transformations to Improve Bootstrapt Intervals
    		68
    38 Parametric Confidence Intervals
    		70
    310 Bootstrap Tests of Significance
    		71
    311 Balanced Bootstrap Sampling
    		74
    313 Further Reading
    		78
    314 Chapter Summary
    		79
    Monte Carlo Methods
    		81
    42 Generalized Monte Carlo Tests
    		84
    43 Implicit Statistical Models
    		86
    44 Applications of Monte Carlo Methods in Biology
    		88
    45 Chapter Summary
    		90
    Some General Considerations
    		93
    52 Power
    		94
    54 Determining a Randomization Distribution Exactly
    		99
    55 The Number of Replications for Confidence Intervals
    		101
    56 More Efficient Bootstrap Sampling Methods
    		103
    58 The Generation of Random Permutations
    		104
    59 Chapter Summary
    		105
    One and TwoSample Tests
    		107
    62 The OneSample Randomization Test
    		112
    63 The TwoSample Randomization Test
    		113
    64 Bootstrap Tests
    		116
    65 Randomizing Residuals
    		117
    66 Comparing the Variation in Two Samples
    		119
    67 A Simulation Study
    		122
    68 The Comparison of Two Samples on Multiple Measurements
    		124
    69 Further Reading
    		129
    610 Chapter Summary
    		130
    Analysis of Variance
    		135
    72 Tests for Constant Variance
    		137
    73 Testing for Mean Differences Using Residuals
    		138
    74 Examples of More Complicated Types of Analysis of Variance
    		143
    75 Procedures for Handling Unequal Variances
    		161
    76 Other Aspects of Analysis of Variance
    		162
    77 Further Reading
    		163
    78 Chapter Summary
    		165
    Regression Analysis
    		169
    82 Randomizing Residuals
    		171
    83 Testing for a Nonzero 𝛽 Value
    		175
    85 Multiple Linear Regression
    		176
    86 Alternative Randomization Methods with Multiple Regression
    		180
    87 Bootstrapping and Jackknifing with Regression
    		196
    88 Further Reading
    		197
    89 Chapter Summary
    		200
    Distance Matrices and Spatial Data
    		203
    103 Meads Randomization Test
    		240
    104 Tests for Randomness Based on Distances
    		245
    105 Testing for an Association between Two Point Patterns
    		247
    106 The BesagDiggle Test
    		248
    107 Tests Using Distances between Points
    		250
    108 Testing for Random Marking
    		252
    109 Further Reading
    		255
    1010 Chapter Summary
    		256
    Time Series
    		261
    112 Randomization Tests for Serial Correlation
    		262
    113 Randomization Tests for Trend
    		267
    114 Randomization Tests for Periodicity
    		274
    115 Irregularly Spaced Series
    		281
    116 Tests on Times of Occurrence
    		283
    117 Discussion on Procedures for Irregular Series
    		285
    118 Bootstrap Methods
    		290
    1110 ModelBased vs MovingBlock Resampling
    		292
    1111 Further Reading
    		294
    1112 Chapter Summary
    		297
    Multivariate Data
    		301
    123 Comparison of Sample Mean Vectors
    		302
    124 ChiSquared Analyses for Count Data
    		312
    125 Comparison of Variations for Several Samples
    		314
    127 Discriminant Function Analysis
    		317
    128 Further Reading
    		320
    129 Chapter Summary
    		321
    Survival and Growth Data
    		325
    132 Bootstrapping for Variable Selection
    		327
    133 Bootstrapping for Model Selection
    		329
    134 Group Comparisons
    		330
    135 Growth Data
    		331
    136 Further Reading
    		336
    137 Chapter Summary
    		337
    Nonstandard Situations
    		341
    143 Alternative Switching Algorithms
    		351
    144 Examining Time Changes in Niche Overlap
    		354
    145 Probing Multivariate Data with Random Skewers
    		360
    146 Ant Species Sizes in Europe
    		365
    147 Chapter Summary
    		370
    Bayesian Methods
    		371
    152 The Gibbs Sampler and Related Methods
    		372
    153 Biological Applications
    		377
    154 Further Reading
    		378
    155 Chapter Summary
    		379
    Final Comments
    		381
    162 Bootstrapping
    		382
    164 Classical vs Bayesian Inference
    		383
    References
    		385
    Software for ComputerIntensive Statistics
    		435
    Author Index
    		439
    Subject Index
    		449
    Copyright

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    Randomization, Bootstrap and Monte Carlo Methods in Biology
    Bryan F.J. Manly
    Limited preview - 2018

    Common terms and phrases

    algorithm alternative analysis of variance application approach approximately assessing bias-corrected bootstrap distribution bootstrap methods bootstrap samples bootstrap test calculated colonies comparing comparison computer-intensive confidence interval confidence limits data values determined differences between months discussed domization Ecology effects Efron estimated evidence example F value F-distribution F-statistic genetic geographical distances give groups habitat hypothesis is true involves islands jackknife jackknife estimate Journal Levene's test linear lizards Manly Mantel test matrix mean difference Monte Carlo methods Monte Carlo test multiple regression multivariate normal distribution null hypothesis observed data observed value parameter percentage percentile method permutation population possible probability procedure random allocation random sample randomiza randomization distribution randomization methods randomization test randomizing observations randomizing residuals Raup regression coefficient resampling sample mean sets of data shown in Table significance level significant results similar simulation situation species standard deviation suggested sum of squares test statistic tion variables variation X₁ zero

    Popular passages

    Page 397 - Faith, DP (1991). Cladistic permutation tests for monophyly and nonmonophyly. Systematic Zoology 40: 366-75.
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    About the author (2006)

    Bryan F.J. Manly (University of Otago, Dunedin, New Zealand), Jorge A. Navarro Alberto

    Bibliographic information

    TitleRandomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition
    Chapman & Hall texts in statistical science series
    Volume 70 of Texts in statistical science
    AuthorBryan F.J. Manly
    Edition3, revised
    PublisherCRC Press, 2006
    ISBN1584885416, 9781584885412
    Length480 pages
    Subjects ›  › 

    Computers / Mathematical & Statistical Software
    Mathematics / Probability & Statistics / General
    Science / Life Sciences / Biology
      
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