Predictive Modeling Applications in Actuarial Science: Predictive Modeling Echniques: Vol 1

Frees, Edward W. (Editor)/ Derrig, Richard A. (Editor)/ Meyers, Glenn (Editor)

ISBN 10: 1107029872 / ISBN 13: 9781107029873

Published by Cambridge Univ Pr, 2014

About this Item

543 pages. 10.00x7.00x1.75 inches. In Stock. This item is printed on demand.

Seller Inventory # __1107029872

Bibliographic Details

• Title: Predictive Modeling Applications in Actuarial Science: Predictive Modeling Echniques: Vol 1
• Publisher: Cambridge Univ Pr
• Publication Date: 2014
• Language: English
• ISBN 10: 1107029872
• ISBN 13: 9781107029873
• Binding: Hardcover
• Condition: Brand New
• Weight: 1.12 kilograms

About this title

Synopsis

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.

Book Description

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 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.