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

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CRC Press, Aug 15, 2006 - Mathematics - 480 pages
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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.
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    Contents

    Randomization
    1
    12 Examples of Randomization Tests
    4
    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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