Ten Projects in Applied Statistics

1. Front Matter

   Pages i-xix

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2. Rat Surgery

   • Peter McCullagh

   Pages 1-14

3. Chain Saws

   • Peter McCullagh

   Pages 15-23

4. Fruit Flies

   • Peter McCullagh

   Pages 25-42

5. Growth Curves

   • Peter McCullagh

   Pages 43-53

6. Louse Evolution

   • Peter McCullagh

   Pages 55-79

7. Time Series I

   • Peter McCullagh

   Pages 81-101

8. Time Series II

   • Peter McCullagh

   Pages 103-116

9. Out of Africa

   • Peter McCullagh

   Pages 117-131

10. Environmental Projects

    • Peter McCullagh

    Pages 133-143

11. Fulmar Fitness

    • Peter McCullagh

    Pages 145-158

12. Basic Concepts

    • Peter McCullagh

    Pages 159-195

13. Principles

    • Peter McCullagh

    Pages 197-209

14. Initial Values

    • Peter McCullagh

    Pages 211-221

15. Probability Distributions

    • Peter McCullagh

    Pages 223-249

16. Gaussian Distributions

    • Peter McCullagh

    Pages 251-277

17. Space-Time Processes

    • Peter McCullagh

    Pages 279-326

18. Likelihood

    • Peter McCullagh

    Pages 327-347

19. Residual Likelihood

    • Peter McCullagh

    Pages 349-361

20. Response Transformation

    • Peter McCullagh

    Pages 363-373

The first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring theme, so variance-components models feature prominently. Statistical pitfalls are illustrated via examples taken from the recent scientific literature. Chapter 11 sets the scene, not just for the second half of the book, but for the book as a whole. It begins by defining fundamental concepts such as baseline, observational unit, experimental unit, covariates and relationships, randomization, treatment assignment, and the role that these play in model formulation. Compatibility of the model with the randomization scheme is crucial. The effect of treatment is invariably modelled as a group action on probability distributions. Technical matters connected with space-time covariance functions, residual likelihood, likelihood ratios, and transformations are discussed in later chapters.

• design of experiments
• data analysis
• linear models
• spatio-temporal processes
• Likelihood function
• Mixture models

• Department of Statistics, University of Chicago, Chicago, USA

  Peter McCullagh

Peter McCullagh is a Professor in the Department of Statistics at the University of Chicago. He is a Fellow of the Royal Society and the American Academy of Arts and Sciences. He won the COPSS Presidents' Award in 1990. He was the recipient of the Royal Statistical Society's Guy Medal in Bronze in 1983 and in Silver in 2005.

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