Randomization, Bootstrap and Monte Carlo Methodology in Biology / Edition 2

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Overview

Modern computer intensive methods are invaluable in the solution of a wide range of problems. The second edition of this popular text illustrates the value of these techniques with particular emphasis on biological applications. Randomization, Bootstrap and Monte Carlo Methods in Biology has been updated with the very latest research, with new chapters on bootstrapping, survival and growth data and Bayesian Markov chain Monte Carlo methods. The examples in the text have been carefully chosen and include the controversial topics of whether there is periodicity in fossil extinction records and whether there is evidence for a relationship between co-occurrences of species on islands. Graduate students and researchers at all levels in biological and medical departments will need to read this book, it will be valued by biologists, biometricians and statisticians working in government departments, research stations and private industry who carry out analyses themselves, or who need to understand others' analyses.
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Editorial Reviews

Booknews
Manly (statistics, U. of Otago, New Zealand) illustrates the value of modern computer intensive methods, emphasizing biological applications and providing new chapters on bootstrapping, survival and growth data and Bayesian Markov chain Monte Carlo methods. Examples include the controversial topics of whether there is periodicity in fossil extinction records and whether there is evidence for a relationship between co-occurences of species on islands. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780412721304
  • Publisher: CRC Press
  • Publication date: 3/1/1997
  • Series: Texts in Statistical Science Series
  • Edition description: Older Edition
  • Edition number: 2
  • Pages: 424
  • Product dimensions: 6.35 (w) x 9.44 (h) x 0.90 (d)

Table of Contents

Preface to the second edition
Preface to the first edition
1 Randomization 1
2 The jackknife 24
3 The bootstrap 34
4 Monte Carlo methods 69
5 Some general considerations 79
6 One- and two-sample tests 91
7 Analysis of variance 117
8 Regression analysis 148
9 Distance matrices and spatial data 172
10 Other analyses on spatial data 205
11 Time series 225
12 Multivariate data 260
13 Survival and growth data 284
14 Non-standard situations 300
15 Bayesian methods 330
16 Conclusion and final comments 338
Appendix Software for computer-intensive statistics 341
References 343
Author index 377
Subject index 388
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