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Randomization, Bootstrap and Monte Carlo Methods in Biology 4th edition [Mīkstie vāki]

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, (University of Otago, Dunedin, New Zealand)
  • Formāts: Paperback / softback, 358 pages, height x width: 234x156 mm, weight: 544 g, 69 Tables, black and white; 30 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Texts in Statistical Science
  • Izdošanas datums: 29-Apr-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367512874
  • ISBN-13: 9780367512873
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Randomization, Bootstrap and Monte Carlo Methods in Biology 4th editionPalielināt
  • Formāts: Paperback / softback, 358 pages, height x width: 234x156 mm, weight: 544 g, 69 Tables, black and white; 30 Illustrations, black and white
  • Sērija : Chapman & Hall/CRC Texts in Statistical Science
  • Izdošanas datums: 29-Apr-2022
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367512874
  • ISBN-13: 9780367512873
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367512873.html
Keywords:
Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online.

Features











Presents an overview of computer-intensive statistical methods and applications in biology





Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods





Makes it easy for biologists, researchers, and students to understand the methods used





Provides information about computer programs and packages to implement calculations, particularly using R code





Includes a large number of real examples from a range of biological disciplines

Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.

Recenzijas

"...This book deals with statistical data simulations in biology...It should be noted that the presentation of the book contains a lot of explanations and justifications that are not limited by mathematical formula. Thus, a biologist can easily understand the basic idea and approach of any statistical method discussed in the book...The book...is very well structured; the presentation of the material is clear and consistent. There are many illustrative examples and exercises. I enjoyed reading this book, and it is clearly included in the list of books that I highly recommend for study in the training of specialists in the field of biostatistics." - Taras Lukashiv, ISCB News, June 2021

Preface to the Fourth Edition xi
Preface to the Third Edition xiii
Preface to the Second Edition xv
Preface to the First Edition xvii
Authors xix
1 Randomization
1(14)
1.1 The Idea of a Randomization Test
1(6)
1.2 Aspects of Randomization Testing Raised by the Example
7(3)
1.3 Confidence Limits by Randomization
10(2)
1.4 Randomization and Observational Studies
12(3)
2 The Bootstrap
15(32)
2.1 Resampling with Replacement
15(1)
2.2 Standard Bootstrap Confidence Limits
15(4)
2.2.1 The Standard Bootstrap Confidence Interval
16(3)
2.3 Simple Percentile Confidence Limits
19(6)
2.4 Bias-Corrected Percentile Confidence Limits
25(4)
2.5 Accelerated Bias-Corrected Percentile Limits
29(7)
2.6 Other Methods for Constructing Confidence Intervals
36(4)
2.7 Transformations to Improve Bootstrap-t Intervals
40(1)
2.8 Parametric Confidence Intervals
41(1)
2.9 A Better Estimate of Bias
41(1)
2.10 Bootstrap Tests of Significance
42(2)
2.11 Balanced Bootstrap Sampling
44(1)
2.12 Bootstrapping with Models for Count Data
45(2)
3 Monte Carlo Methods
47(8)
3.1 Monte Carlo Tests
47(2)
3.2 Generalized Monte Carlo Tests
49(4)
3.3 Implicit Statistical Models
53(2)
4 Some General Considerations
55(12)
4.1 Questions about Computer-Intensive Methods
55(1)
4.2 Power
55(1)
4.3 Number of Random Sets of Data Needed for a Test
56(4)
4.4 Determining a Randomization Distribution Exactly
60(2)
4.5 The Number of Replications for Confidence Intervals
62(2)
4.6 More Efficient Bootstrap Sampling Methods
64(1)
4.7 The Generation of Pseudo-Random Numbers
64(1)
4.8 The Generation of Random Permutations
65(2)
5 One- and Two-Sample Tests
67(22)
5.1 The Paired Comparisons Design
67(4)
5.2 The One-Sample Randomization Test
71(1)
5.3 The Two-Sample Randomization Test
72(3)
5.4 Bootstrap Tests
75(1)
5.5 Randomizing Residuals
76(2)
5.6 Comparing the Variation in Two Samples
78(2)
5.7 A Simulation Study
80(3)
5.8 Comparison of Two Samples on Multiple Measurements
83(6)
6 Analysis of Variance
89(30)
6.1 One-Factor Analysis of Variance
89(2)
6.2 Tests for Constant Variance
91(1)
6.3 Testing for Mean Differences Using Residuals
92(4)
6.4 Examples of More Complicated Types of Analysis of Variance
96(17)
6.5 Procedures for Handling Unequal Variances
113(1)
6.6 Other Aspects of Analysis of Variance
114(5)
Exercises
115(4)
7 Regression Analysis
119(28)
7.1 Simple Linear Regression
119(2)
7.2 Randomizing Residuals
121(3)
7.3 Testing for a Non-Zero B Value
124(1)
7.4 Confidence Limits for B
124(1)
7.5 Multiple Linear Regression
125(3)
7.6 Alternative Randomization Methods with Multiple Regression
128(15)
7.7 Bootstrapping with Regression
143(1)
7.8 Further Reading
143(4)
Exercises
145(2)
8 Distance Matrices and Spatial Data
147(32)
8.1 Testing for Association between Distance Matrices
147(1)
8.2 The Mantel Test
148(2)
8.3 Sampling the Randomization Distribution
150(2)
8.4 Confidence Limits for Regression Coefficients
152(2)
8.5 The Multiple Mantel Test
154(1)
8.6 Other Approaches with More Than Two Matrices
155(16)
8.7 Further Reading
171(8)
Exercises
173(6)
9 Other Analyses on Spatial Data
179(18)
9.1 Spatial Data Analysis
179(1)
9.2 The Study of Spatial Point Patterns
179(1)
9.3 Mead's Randomization Test
180(4)
9.4 Tests for Randomness Based on Distances
184(2)
9.5 Testing for an Association between Two Point Patterns
186(1)
9.6 The Besag-Diggle Test
187(2)
9.7 Tests Using Distances between Points
189(2)
9.8 Testing for Random Marking
191(2)
9.9 Further Reading
193(4)
Exercises
196(1)
10 Time Series
197(32)
10.1 Randomization and Time Series
197(1)
10.2 Randomization Tests for Serial Correlation
198(5)
10.3 Randomization Tests for Trend
203(5)
10.4 Randomization Tests for Periodicity
208(7)
10.5 Irregularly Spaced Series
215(2)
10.6 Tests on Times of Occurrence
217(2)
10.7 Discussion on Procedures for Irregular Series
219(4)
10.8 Bootstrap Methods
223(1)
10.9 Monte Carlo Methods
223(2)
10.10 Model-Based versus Moving Block Resampling
225(2)
10.11 Further Reading
227(2)
11 Survival and Growth Data
229(14)
11.1 Bootstrapping Survival Data
229(2)
11.2 Bootstrapping for Variable Selection
231(2)
11.3 Bootstrapping for Model Selection
233(1)
11.4 Group Comparisons
234(1)
11.5 Growth Data
234(5)
11.6 Further Reading
239(4)
Exercises
242(1)
12 Non-Standard Situations
243(28)
12.1 The Construction of Tests in Non-Standard Situations
243(1)
12.2 Species Co-Occurrences on Islands
243(10)
12.3 Alternative Switching Algorithms
253(2)
12.4 Examining Time Changes in Niche Overlap
255(7)
12.5 Probing Multivariate Data with Random Skewers
262(4)
12.6 Ant Species Sizes in Europe
266(5)
13 Bayesian Methods
271(8)
13.1 The Bayesian Approach to Data Analysis
271(1)
13.2 The Gibbs Sampler and Related Methods
272(4)
13.3 Biological Applications
276(1)
13.4 Further Reading
277(2)
14 Conclusion and Final Comments
279(4)
14.1 Randomization
279(1)
14.2 Bootstrapping
279(1)
14.3 Monte Carlo Methods in General
280(1)
14.4 Classical versus Bayesian Inference
281(2)
Appendix: Software for Computer-Intensive Statistics 283(16)
References 299(32)
Index 331
Bryan F.J. Manly is an international expert on the analysis of data from environmental and ecological studies and also data from studies in other subject areas. He is the author of seven books on statistical methods, and is one of the two Chief Editors of the international journal, Environmental and Ecological Statistics.

Jorge A. Navarro Alberto is in the Department of Tropical Ecology at the Autonomous University of Yucatan, Mexico, with research interests in ecological and environmental statistics and computer-intensive methods. In particular, he has contributed to the development of randomization algorithms for the analysis of ecological data. He has more than thirty years of experience teaching statistics for biologists, marine biologists, and natural resource managers in Mexico, and also as a visiting professor at the Department of Mathematics and Statistics in the University of Wyoming.