Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Variation
- 3 Uncertainty
- 4 Likelihood
- 5 Models
- 6 Stochastic Models
- 7 Estimation and Hypothesis Testing
- 8 Linear Regression Models
- 9 Designed Experiments
- 10 Nonlinear Regression Models
- 11 Bayesian Models
- 12 Conditional and Marginal Inference
- Appendix A Practicals
- Bibliography
- Name Index
- Example Index
- Index
Appendix A - Practicals
Published online by Cambridge University Press: 29 March 2011
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Variation
- 3 Uncertainty
- 4 Likelihood
- 5 Models
- 6 Stochastic Models
- 7 Estimation and Hypothesis Testing
- 8 Linear Regression Models
- 9 Designed Experiments
- 10 Nonlinear Regression Models
- 11 Bayesian Models
- 12 Conditional and Marginal Inference
- Appendix A Practicals
- Bibliography
- Name Index
- Example Index
- Index
Summary
The list below gives key words for practicals written in the statistical language S and intended to accompany the chapters of the book. The practicals themselves may be downloaded from
http://statwww.epfl.ch/people/∼davison/SM
together with a library of functions and data.
Variation
Speed of light data. Exploratory data analysis.
Maths marks data. Brush and spin plots.
Probability plots for simulated data.
Illustration of central limit theorem using simulated data.
Data on air-conditioning failures. Exponential probability plots.
Uncertainty
Properties of half-normal distribution. Half-normal plot.
Simulation of Student t statistic, following original derivation.
Simulation of Wiener process and Brownian bridge.
Normal random number generation by summing uniform variables.
Implementation and assessment of a linear congruential generator.
Coverage of Student t confidence interval under various scenarios.
Likelihood
Loss of information due to rounding of normal data.
Birth data. Maximum likelihood estimation for Poisson and gamma models. Assessment of fit.
Data on sizes of groups of people. Maximum likelihood fit of truncated Poisson distribution. Pearson's statistic.
α-particle data. Maximum likelihood fit of Poisson process model.
Blood group data. Maximum likelihood fit of multinomial model.
Generalized Pareto distribution. Nonregular estimation of endpoint.
Models
Boiling point of water data. Straight-line regression.
Survival data on leukaemia. Exponential and Weibull models.
HUS data. EM algorithm for mixture of Poisson distributions.
EM algorithm for mixture of normal distributions.
- Type
- Chapter
- Information
- Statistical Models , pp. 696 - 698Publisher: Cambridge University PressPrint publication year: 2003