Effective Statistical Learning Methods for Actuaries I
GLMs and Extensions
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Beschreibung
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 insurancedata analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
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.
Features numerous examples and case studies in P&C, Life and Health insurance Provides a broad and self-contained presentation of insurance data analytics techniques, from classical GLMs to neural networks Addresses many specific issues which arise in insurance data analysis Can be used as course material, for CPD programs or for self-study Complements the existing literature on GLMs in insurance Written by actuaries for actuaries Based on more than a decade of lectures and consulting projects on the topic, by the three authors
Autor*in
Michel Denuit
Themen in »Effective Statistical Learning Methods for Actuaries I«
Insurance risk classification Supervised learning Exponential dispersion model Regression analysis GLM