Skip to content
Register Sign in Wishlist

Predictive Modeling Applications in Actuarial Science

Volume 1. Predictive Modeling Techniques

$68.00 USD

Part of International Series on Actuarial Science

Edward W. Frees, Richard A. Derrig, Marjorie Rosenberg, Montserrat Guillen, Jean-Philippe Boucher, Curtis Gary Dean, Katrien Antonio, Yanwei Zhang, Vytaras Brazauskas, Harald Dornheim, Ponmalar Ratnam, Peng Shi, Eike Brechmann, Claudia Czado, Louise Francis, Brian Hartman, Luis Nieto-Barajas, Enrique de Alba, Patrick L. Brockett, Shuo-Li Chuang, Utai Pitaktong, Katrien Antonio, Piet de Jong, Greg Taylor, Jim Robinson, Bruce Jones, Weijia Wu
View all contributors
  • Date Published: July 2014
  • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • format: Adobe eBook Reader
  • isbn: 9781139989992

$ 68.00 USD
Adobe eBook Reader

You will be taken to ebooks.com for this purchase
Buy eBook Add to wishlist

Other available formats:
Hardback


Looking for an inspection copy?

This title is not currently available on inspection

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

    • Provides a link between data analysis and data modeling by explaining the role of a model
    • Introduces advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data
    • Aimed at both novice and seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice
    Read more

    Reviews & endorsements

    'With contributions coming from a wide variety of researchers, professors, and actuaries - including several CAS Fellows - it's clear that this book will be valuable for any P and C actuary whose main concern is using predictive modeling in his or her own work.' David Zornek, Actuarial Review

    Customer reviews

    Not yet reviewed

    Be the first to review

    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: July 2014
    • format: Adobe eBook Reader
    • isbn: 9781139989992
    • contains: 120 b/w illus. 94 tables 26 exercises
    • availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
  • Table of Contents

    1. Predictive modeling in actuarial science Edward W. Frees and Richard A. Derrig
    Part I. Predictive Modeling Foundations:
    2. Overview of linear models Marjorie Rosenberg
    3. Regression with categorical dependent variables Montserrat Guillen
    4. Regression with count-dependent variables Jean-Philippe Boucher
    5. Generalized linear models Curtis Gary Dean
    6. Frequency and severity models Edward W. Frees
    Part II. Predictive Modeling Methods:
    7. Longitudinal and panel data models Edward W. Frees
    8. Linear mixed models Katrien Antonio and Yanwei Zhang
    9. Credibility and regression modeling Vytaras Brazauskas, Harald Dornheim and Ponmalar Ratnam
    10. Fat-tailed regression models Peng Shi
    11. Spatial modeling Eike Brechmann and Claudia Czado
    12. Unsupervised learning Louise Francis
    Part III. Bayesian and Mixed Modeling:
    13. Bayesian computational methods Brian Hartman
    14. Bayesian regression models Luis Nieto-Barajas and Enrique de Alba
    15. Generalized additive models and nonparametric regression Patrick L. Brockett, Shuo-Li Chuang and Utai Pitaktong
    16. Non-linear mixed models Katrien Antonio and Yanwei Zhang
    Part IV. Longitudinal Modeling:
    17. Time series analysis Piet de Jong
    18. Claims triangles/loss reserves Greg Taylor
    19. Survival models Jim Robinson
    20. Transition modeling Bruce Jones and Weijia Wu.

  • Resources for

    Predictive Modeling Applications in Actuarial Science

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to lecturers whose faculty status has been verified. To gain access to locked resources, lecturers should sign in to or register for a Cambridge user account.

    Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other lecturers may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

    Supplementary resources are subject to copyright. Lecturers are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

    If you are having problems accessing these resources please contact lecturers@cambridge.org.

  • Editors

    Edward W. Frees, University of Wisconsin, Madison

    Richard A. Derrig, Temple University, Philadelphia

    Glenn Meyers, ISO Innovative Analytics, New Jersey

    Contributors

    Edward W. Frees, Richard A. Derrig, Marjorie Rosenberg, Montserrat Guillen, Jean-Philippe Boucher, Curtis Gary Dean, Katrien Antonio, Yanwei Zhang, Vytaras Brazauskas, Harald Dornheim, Ponmalar Ratnam, Peng Shi, Eike Brechmann, Claudia Czado, Louise Francis, Brian Hartman, Luis Nieto-Barajas, Enrique de Alba, Patrick L. Brockett, Shuo-Li Chuang, Utai Pitaktong, Katrien Antonio, Piet de Jong, Greg Taylor, Jim Robinson, Bruce Jones, Weijia Wu

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.
×