Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a...

Buy Now From Amazon

Product Review

Predictive modeling uses data to forecast future events. It exploits relationships between explanatory variables and the predicted variables from past occurrences to predict future outcomes. Forecasting financial events is a core skill that actuaries routinely apply in insurance and other risk-management applications. 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 to gain a competitive advantage in situations with complex data. Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing on property and casualty insurance. Readers are exposed to a variety of techniques in concrete, real-life contexts that demonstrate their value and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.

Similar Products

Predictive Modeling Applications in Actuarial Science: Volume 1, Predictive Modeling Techniques (International Series on Actuarial Science)Regression Modeling with Actuarial and Financial Applications (International Series on Actuarial Science)Applied Regression Analysis and Generalized Linear Models (NULL)An R Companion to Applied RegressionR for Marketing Research and Analytics (Use R!)Generalized Linear Models for Insurance Data (International Series on Actuarial Science)Data Analysis Using Regression and Multilevel/Hierarchical ModelsWeapons of Math Destruction: How Big Data Increases Inequality and Threatens DemocracyComputational Actuarial Science with R (Chapman & Hall/CRC The R Series)An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)