Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)
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Language: English
Published by Cambridge University Press, 2014
Series: Book 8 of 16 - International Series on Actuarial Science
- Hardcover
- New

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- Title
- Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)
- Publisher
- Cambridge University Press
- Publication year
- 2014
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1107029872
- ISBN 13
- 9781107029873
- Series
- Book 8 of 16: International Series on Actuarial Science
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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