
International Series on Actuarial Science Ser.: Predictive Modeling Applications
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eBay-Artikelnr.:396083145332
Artikelmerkmale
- Artikelzustand
- ISBN
- 9781107029873
Über dieses Produkt
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1107029872
ISBN-13
9781107029873
eBay Product ID (ePID)
177553978
Product Key Features
Number of Pages
563 Pages
Language
English
Publication Name
Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques
Subject
Future Studies, Statistics, Insurance / General
Publication Year
2014
Type
Textbook
Subject Area
Social Science, Business & Economics
Series
International Series on Actuarial Science Ser.
Format
Hardcover
Dimensions
Item Height
1.5 in
Item Weight
39.5 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2013-049070
Dewey Edition
23
Reviews
"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
Illustrated
Yes
Dewey Decimal
368/.01
Table Of Content
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.
Synopsis
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. It 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., 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., 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 practicing analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes life-long 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.
LC Classification Number
HG8781 .P74 2014
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