Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions (1st ed. 2019) (Springer Actuarial)
By: and and
Sign Up Now!
Already a Member? Log In
You must be logged into UK education collection to access this title.
Learn about membership options,
or view our freely available titles.
- Synopsis
- This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
- Copyright:
- 2019
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783030258207
- Related ISBNs:
- 9783030258191
- Publisher:
- Springer International Publishing
- Date of Addition:
- 08/27/22
- Copyrighted By:
- Springer Nature Switzerland AG
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Business and Finance, Mathematics and Statistics
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
Reviews
Other Books
- by Michel Denuit
- by Donatien Hainaut
- by Julien Trufin
- in Nonfiction
- in Business and Finance
- in Mathematics and Statistics