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Effective statistical learning metho...

Denuit, M.

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Effective statistical learning methods for actuaries.Michel Denuit, Donatien Hainaut, Julien Trufin.
Author:	Denuit, M.
other author:	Hainaut, Donatien,
Published:	Cham, Switzerland :Springer,c2019.
Description:	xvi, 441 p. :ill. (some col.) ;24 cm.
Subject:	Actuarial science.
ISBN:	9783030258191 (pbk.) :

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

Effective statistical learning methods for actuaries.I,GLMs and extensions /Michel Denuit, Donatien Hainaut, Julien Trufin. - Cham, Switzerland :Springer,c2019. - xvi, 441 p. :ill. (some col.) ;24 cm. - Springer actuarial lecture notes.

Includes bibliographical references.

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: 9783030258191 (pbk.) :EUR$44.99Subjects--Topical Terms:

603592
Actuarial science.

LC Class. No.: HG8781 / .D46 2019

Dewey Class. No.: 368.01

Effective statistical learning methods for actuaries.I,GLMs and extensions /
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020 $z 9783030258207 (ebk.)
035 $a (OCoLC)1151771139 $z (OCoLC)1105357298
035 $a on1151771139
040 $a ISU $b eng $c ISU $d OCLCO $d YDXIT $d BDX $d YDX $d OCLCF $d OCLCQ
050 4 $a HG8781 $b .D46 2019
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100 1 $a Denuit, M. $q (Michel). $3 436621
245 1 0 $a Effective statistical learning methods for actuaries. $n I, $p GLMs and extensions / $c Michel Denuit, Donatien Hainaut, Julien Trufin.
260 $a Cham, Switzerland : $b Springer, $c c2019.
300 $a xvi, 441 p. : $b ill. (some col.) ; $c 24 cm.
490 0 $a Springer actuarial lecture notes
504 $a Includes bibliographical references.
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.
650 0 $a Actuarial science. $3 603592
650 0 $a Insurance $x Statistical methods. $3 183041
650 0 $a Linear models (Statistics) $3 181866
650 0 $a Neural networks (Computer science) $3 181982
700 1 $a Hainaut, Donatien, $e author. $3 857872
700 1 $a Trufin, Julien, $e author. $3 857873