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Effective statistical learning metho...

Denuit, Michel.

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Effective statistical learning methods for actuaries I = GLMs and extensions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Effective statistical learning methods for actuaries I/ by Michel Denuit, Donatien Hainaut, Julien Trufin.
Reminder of title:	GLMs and extensions /
Author:	Denuit, Michel.
other author:	Hainaut, Donatien.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xvi, 441 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Actuarial science. -
Online resource:	https://doi.org/10.1007/978-3-030-25820-7
ISBN:	9783030258207

Effective statistical learning methods for actuaries I = GLMs and extensions /
Denuit, Michel.

Effective statistical learning methods for actuaries IGLMs and extensions /[electronic resource] :by Michel Denuit, Donatien Hainaut, Julien Trufin. - Cham :Springer International Publishing :2019. - xvi, 441 p. :ill. (some col.), digital ;24 cm. - Springer actuarial,2523-3270. - Springer actuarial..

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.

ISBN: 9783030258207

Standard No.: 10.1007/978-3-030-25820-7doiSubjects--Topical Terms:

1536028
Actuarial science.

LC Class. No.: HG8781 / .D45 2019

Dewey Class. No.: 368.01

Effective statistical learning methods for actuaries I = GLMs and extensions /
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520 $a 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.
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