Skip to main content Accessibility help
×
Hostname: page-component-77c89778f8-sh8wx Total loading time: 0 Render date: 2024-07-17T13:40:36.909Z Has data issue: false hasContentIssue false

Appendix A - Practicals

Published online by Cambridge University Press:  29 March 2011

A. C. Davison
Affiliation:
Swiss Federal Institute of Technology, Lausanne
Get access

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

  1. Speed of light data. Exploratory data analysis.

  2. Maths marks data. Brush and spin plots.

  3. Probability plots for simulated data.

  4. Illustration of central limit theorem using simulated data.

  5. Data on air-conditioning failures. Exponential probability plots.

Uncertainty

  1. Properties of half-normal distribution. Half-normal plot.

  2. Simulation of Student t statistic, following original derivation.

  3. Simulation of Wiener process and Brownian bridge.

  4. Normal random number generation by summing uniform variables.

  5. Implementation and assessment of a linear congruential generator.

  6. Coverage of Student t confidence interval under various scenarios.

Likelihood

  1. Loss of information due to rounding of normal data.

  2. Birth data. Maximum likelihood estimation for Poisson and gamma models. Assessment of fit.

  3. Data on sizes of groups of people. Maximum likelihood fit of truncated Poisson distribution. Pearson's statistic.

  4. α-particle data. Maximum likelihood fit of Poisson process model.

  5. Blood group data. Maximum likelihood fit of multinomial model.

  6. Generalized Pareto distribution. Nonregular estimation of endpoint.

Models

  1. Boiling point of water data. Straight-line regression.

  2. Survival data on leukaemia. Exponential and Weibull models.

  3. HUS data. EM algorithm for mixture of Poisson distributions.

  4. EM algorithm for mixture of normal distributions.

Type
Chapter
Information
Statistical Models , pp. 696 - 698
Publisher: Cambridge University Press
Print publication year: 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Practicals
  • A. C. Davison, Swiss Federal Institute of Technology, Lausanne
  • Book: Statistical Models
  • Online publication: 29 March 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511815850.014
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Practicals
  • A. C. Davison, Swiss Federal Institute of Technology, Lausanne
  • Book: Statistical Models
  • Online publication: 29 March 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511815850.014
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Practicals
  • A. C. Davison, Swiss Federal Institute of Technology, Lausanne
  • Book: Statistical Models
  • Online publication: 29 March 2011
  • Chapter DOI: https://doi.org/10.1017/CBO9780511815850.014
Available formats
×