Generalized Linear Models For Insurance Data
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Generalized Linear Models For Insurance Data
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Author : Piet de Jong
language : en
Publisher: Cambridge University Press
Release Date : 2008-02-28
Generalized Linear Models For Insurance Data written by Piet de Jong and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-28 with Business & Economics categories.
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
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Author : Esbjörn Ohlsson
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-03-18
Non Life Insurance Pricing With Generalized Linear Models written by Esbjörn Ohlsson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-03-18 with Mathematics categories.
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.
Generalized Linear Models For Insurance Rating
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Author : Mark Goldburd
language : en
Publisher:
Release Date : 2016-06-08
Generalized Linear Models For Insurance Rating written by Mark Goldburd and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-08 with categories.
Effective Statistical Learning Methods For Actuaries I
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2019-09-03
Effective Statistical Learning Methods For Actuaries I written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Business & Economics categories.
This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
Generalized Linear Models For Insurance Data
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Author : Piet de Jong
language : en
Publisher: Cambridge University Press
Release Date : 2008-04-02
Generalized Linear Models For Insurance Data written by Piet de Jong and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-04-02 with Business & Economics categories.
All techniques illustrated on data sets relevant to insurance; SAS code and output, data sets, exercise solutions on website
Regression Modeling With Actuarial And Financial Applications
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2010
Regression Modeling With Actuarial And Financial Applications written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Predictive Modeling Applications In Actuarial Science Volume 2 Case Studies In Insurance
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2016-07-27
Predictive Modeling Applications In Actuarial Science Volume 2 Case Studies In Insurance written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-27 with Business & Economics categories.
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 For Insurance Data
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Author : Piet de Jong
language : en
Publisher:
Release Date : 2008
Generalized Linear Models For Insurance Data written by Piet de Jong and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Insurance categories.
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.
Generalized Linear Models Second Edition
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Author : P. McCullagh
language : en
Publisher: CRC Press
Release Date : 1989-08-01
Generalized Linear Models Second Edition written by P. McCullagh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-08-01 with Mathematics categories.
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 other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.
Multivariate Statistical Modelling Based On Generalized Linear Models
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Author : Ludwig Fahrmeir
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Multivariate Statistical Modelling Based On Generalized Linear Models written by Ludwig Fahrmeir and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
Classical statistical models for regression, time series and longitudinal data provide well-established tools for approximately normally distributed vari ables. Enhanced by the availability of software packages these models dom inated the field of applications for a long time. With the introduction of generalized linear models (GLM) a much more flexible instrument for sta tistical modelling has been created. The broad class of GLM's includes some of the classicallinear models as special cases but is particularly suited for categorical discrete or nonnegative responses. The last decade has seen various extensions of GLM's: multivariate and multicategorical models have been considered, longitudinal data analysis has been developed in this setting, random effects and nonparametric pre dictors have been included. These extended methods have grown around generalized linear models but often are no longer GLM's in the original sense. The aim of this book is to bring together and review a large part of these recent advances in statistical modelling. Although the continuous case is sketched sometimes, thoughout the book the focus is on categorical data. The book deals with regression analysis in a wider sense including not only cross-sectional analysis but also time series and longitudinal data situations. We do not consider problems of symmetrical nature, like the investigation of the association structure in a given set of variables. For example, log-linear models for contingency tables, which can be treated as special cases of GLM's are totally omitted. The estimation approach that is primarily considered in this book is likelihood-based.