Generalized Linear Models for Insurance Data
Generalized Linear Models for Insurance Data

Author: Piet de Jong

Publisher: Cambridge University Press

Published: 2008-02-28

Total Pages: 207

ISBN-13: 1139470477

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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.

Non-Life Insurance Pricing with Generalized Linear Models
Non-Life Insurance Pricing with Generalized Linear Models

Author: Esbjörn Ohlsson

Publisher: Springer Science & Business Media

Published: 2010-03-18

Total Pages: 181

ISBN-13: 3642107915

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Non-life insurance pricing is the art of setting the price of an insurance policy, taking into consideration varoius properties of the insured object and the policy holder. Introduced by British actuaries generalized linear models (GLMs) have become today a the standard aproach for tariff analysis. The book focuses on methods based on GLMs that have been found useful in actuarial practice and provides a set of tools for a tariff analysis. Basic theory of GLMs in a tariff analysis setting is presented with useful extensions of standarde GLM theory that are not in common use. The book meets the European Core Syllabus for actuarial education and is written for actuarial students as well as practicing actuaries. To support reader real data of some complexity are provided at www.math.su.se/GLMbook.

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

Author: Edward W. Frees

Publisher: Cambridge University Press

Published: 2016-07-27

Total Pages: 337

ISBN-13: 1316720527

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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.

Generalized Linear Models With Examples in R
Generalized Linear Models With Examples in R

Author: Peter K. Dunn

Publisher: Springer

Published: 2018-11-10

Total Pages: 573

ISBN-13: 1441901183

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This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities. The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. Other features include: • Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals • Nearly 100 data sets in the companion R package GLMsData • Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session

Stochastic Loss Reserving Using Generalized Linear Models
Stochastic Loss Reserving Using Generalized Linear Models

Author: Greg Taylor

Publisher:

Published: 2016-05-04

Total Pages: 100

ISBN-13: 9780996889704

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In this monograph, authors Greg Taylor and Gráinne McGuire discuss generalized linear models (GLM) for loss reserving, beginning with strong emphasis on the chain ladder. The chain ladder is formulated in a GLM context, as is the statistical distribution of the loss reserve. This structure is then used to test the need for departure from the chain ladder model and to consider natural extensions of the chain ladder model that lend themselves to the GLM framework.

Predictive Modeling Applications in Actuarial Science
Predictive Modeling Applications in Actuarial Science

Author: Edward W. Frees

Publisher: Cambridge University Press

Published: 2014-07-28

Total Pages: 565

ISBN-13: 1107029872

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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.

Generalized Linear Models
Generalized Linear Models

Author: P. McCullagh

Publisher: Routledge

Published: 2019-01-22

Total Pages: 536

ISBN-13: 1351445847

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The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot

Modern Actuarial Risk Theory
Modern Actuarial Risk Theory

Author: Rob Kaas

Publisher: Springer Science & Business Media

Published: 2008-12-03

Total Pages: 394

ISBN-13: 3540867368

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Modern Actuarial Risk Theory contains what every actuary needs to know about non-life insurance mathematics. It starts with the standard material like utility theory, individual and collective model and basic ruin theory. Other topics are risk measures and premium principles, bonus-malus systems, ordering of risks and credibility theory. It also contains some chapters about Generalized Linear Models, applied to rating and IBNR problems. As to the level of the mathematics, the book would fit in a bachelors or masters program in quantitative economics or mathematical statistics. This second and.