Skip to content
Register Sign in Wishlist

Statistical Learning for Biomedical Data

$139.00

Part of Practical Guides to Biostatistics and Epidemiology

  • Date Published: February 2011
  • availability: Available
  • format: Hardback
  • isbn: 9780521875806

$ 139.00
Hardback

Add to cart Add to wishlist

Other available formats:
Paperback, eBook


Looking for an inspection copy?

This title is not currently available for inspection. However, if you are interested in the title for your course we can consider offering an inspection copy. To register your interest please contact asiamktg@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research. This introduction brings learning machine techniques to the biomedical world in an accessible way, explaining the underlying principles in nontechnical language and using extensive examples and figures. The authors connect these new methods to familiar techniques by showing how to use the learning machine models to generate smaller, more easily interpretable traditional models. Coverage includes single decision trees, multiple-tree techniques such as Random Forests™, neural nets, support vector machines, nearest neighbors and boosting.

    • Free open-source computer code is available online
    • Brings valuable new ideas from probability and computer science into the biomedical world to provide more accurate predictions
    • Plain-language approach makes the techniques more accessible
    Read more

    Reviews & endorsements

    'The book is well written and provides nice graphics and numerous applications.' Michael R. Chernick, Technometrics

    Customer reviews

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: February 2011
    • format: Hardback
    • isbn: 9780521875806
    • length: 298 pages
    • dimensions: 253 x 179 x 21 mm
    • weight: 0.75kg
    • contains: 47 b/w illus. 25 tables
    • availability: Available
  • Table of Contents

    Preface
    Acknowledgements
    Part I. Introduction:
    1. Prologue
    2. The landscape of learning machines
    3. A mangle of machines
    4. Three examples and several machines
    Part II. A Machine Toolkit:
    5. Logistic regression
    6. A single decision tree
    7. Random forests – trees everywhere
    Part III. Analysis Fundamentals:
    8. Merely two variables
    9. More than two variables
    10. Resampling methods
    11. Error analysis and model validation
    Part IV. Machine Strategies:
    12. Ensemble methods – let's take a vote
    13. Summary and conclusions
    References
    Index.

  • Instructors have used or reviewed this title for the following courses

    • Analysis of Categorical Data
    • Applied Epidemiology
    • Fundamentals of Biostatistics
    • Statistics for Health Sciences
  • Authors

    James D. Malley, National Institutes of Health, Maryland
    James D. Malley is a Research Mathematical Statistician in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.

    Karen G. Malley, Malley Research Programming, Maryland
    Karen G. Malley is president of Malley Research Programming, Inc. in Rockville, Maryland, providing statistical programming services to the pharmaceutical industry and the National Institutes of Health. She also serves on the global council of the Clinical Data Interchange Standards Consortium (CDISC) user network, and the steering committee of the Washington, DC area CDISC user network.

    Sinisa Pajevic, National Institutes of Health, Maryland
    Sinisa Pajevic is a Staff Scientist in the Mathematical and Statistical Computing Laboratory, Division of Computational Bioscience, Center for Information Technology, at the National Institutes of Health.

related journals

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×