Generalized Linear Models for Insurance Data

Author: Piet de Jong
Publisher: Cambridge University Press
ISBN: 1139470477
Format: PDF, Docs
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This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

Predictive Modeling Applications in Actuarial Science Volume 2 Case Studies in Insurance

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1316720527
Format: PDF, ePub, Docs
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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.

Predictive Modeling Applications in Actuarial Science

Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1107029872
Format: PDF, ePub
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This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

Stochastische Risikomodellierung und statistische Methoden

Author: Torsten Becker
Publisher: Springer-Verlag
ISBN: 3662494078
Format: PDF
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Dieses Buch vereinigt Konzepte und Methoden der stochastischen Modellbildung, der statistischen Analyse und der aktuariellen Anwendung in einem Band.Dabei wird eine kompakte, aber dennoch für Theoretiker wie Praktiker gut verständliche und interessante Darstellung der Themengebiete Risikobewertung, explorative Datenanalyse, Simulation, Stochastische Modelle und Prozesse, verallgemeinerte lineare Regression, biometrische Modelle und Credibility gegeben.Zahlreiche Beispiele illustrieren die Anwendung der dargestellten Konzepte in der aktuariellen Praxis, wobei auf Modelle aus der Personenversicherung, Sachversicherungs- und Finanzmathematik eingegangen wird.

Regression

Author: Ludwig Fahrmeir
Publisher: Springer-Verlag
ISBN: 3642018378
Format: PDF
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In dem Band beschreiben die Autoren erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten und anwendungsorientierten Form. Um Lesern die Analyse eigener Fragestellungen zu ermöglichen, demonstrieren sie die praktische Anwendung der Konzepte und Methoden anhand ausführlicher Fallstudien. Geeignet für Studierende der Statistik sowie für Wissenschaftler und Praktiker, zum Beispiel in den Wirtschafts- und Sozialwissenschaften, der Bioinformatik und -statistik, Ökonometrie und Epidemiologie.