Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Effective Statistical Learning Metho...
~
Trufin, Julien.
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 I/ by Michel Denuit, Donatien Hainaut, Julien Trufin.
Reminder of title:
GLMs and Extensions /
Author:
Denuit, Michel.
other author:
Hainaut, Donatien.
Description:
XVI, 441 p. 82 illus., 23 illus. in color.online resource. :
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 I
GLMs and Extensions /[electronic resource] :by Michel Denuit, Donatien Hainaut, Julien Trufin. - 1st ed. 2019. - XVI, 441 p. 82 illus., 23 illus. in color.online resource. - Springer Actuarial Lecture Notes,2523-3289. - Springer Actuarial Lecture Notes,.
Preface -- Part I: LOSS MODELS.-1. Insurance Risk Classification.-Exponential Dispersion (ED) Distributions.-3.-Maximum Likelihood Estimation.-Part II LINEAR MODELS.-4. Generalized Linear Models (GLMs) -- 5.-Over-dispersion, credibility adjustments, mixed models, and regularization.-Part III ADDITIVE MODELS -- 6 Generalized Additive Models (GAMs) -- 7. Beyond Mean Modeling: Double GLMs and GAMs for Location, Scale and Shape (GAMLSS) -- Part IV SPECIAL TOPICS -- 8. Some Generalized Non-Linear Models (GNMs) -- 9 Extreme Value Models -- 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: 9783030258207
Standard No.: 10.1007/978-3-030-25820-7doiSubjects--Topical Terms:
943795
Actuarial science.
LC Class. No.: HG8779-8793
Dewey Class. No.: 368.01
Effective Statistical Learning Methods for Actuaries I = GLMs and Extensions /
LDR
:03116nam a22003975i 4500
001
1006837
003
DE-He213
005
20200701201123.0
007
cr nn 008mamaa
008
210106s2019 gw | s |||| 0|eng d
020
$a
9783030258207
$9
978-3-030-25820-7
024
7
$a
10.1007/978-3-030-25820-7
$2
doi
035
$a
978-3-030-25820-7
050
4
$a
HG8779-8793
072
7
$a
KFFN
$2
bicssc
072
7
$a
BUS033000
$2
bisacsh
072
7
$a
KFFN
$2
thema
082
0 4
$a
368.01
$2
23
100
1
$a
Denuit, Michel.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1300484
245
1 0
$a
Effective Statistical Learning Methods for Actuaries I
$h
[electronic resource] :
$b
GLMs and Extensions /
$c
by Michel Denuit, Donatien Hainaut, Julien Trufin.
250
$a
1st ed. 2019.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
XVI, 441 p. 82 illus., 23 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Springer Actuarial Lecture Notes,
$x
2523-3289
505
0
$a
Preface -- Part I: LOSS MODELS.-1. Insurance Risk Classification.-Exponential Dispersion (ED) Distributions.-3.-Maximum Likelihood Estimation.-Part II LINEAR MODELS.-4. Generalized Linear Models (GLMs) -- 5.-Over-dispersion, credibility adjustments, mixed models, and regularization.-Part III ADDITIVE MODELS -- 6 Generalized Additive Models (GAMs) -- 7. Beyond Mean Modeling: Double GLMs and GAMs for Location, Scale and Shape (GAMLSS) -- Part IV SPECIAL TOPICS -- 8. Some Generalized Non-Linear Models (GNMs) -- 9 Extreme Value Models -- 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
943795
650
0
$a
Statistics .
$3
1253516
650
1 4
$a
Actuarial Sciences.
$3
884190
650
2 4
$a
Statistics for Business, Management, Economics, Finance, Insurance.
$3
1211158
700
1
$a
Hainaut, Donatien.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1300485
700
1
$a
Trufin, Julien.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1300486
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030258191
776
0 8
$i
Printed edition:
$z
9783030258214
830
0
$a
Springer Actuarial Lecture Notes,
$x
2523-3289
$3
1300487
856
4 0
$u
https://doi.org/10.1007/978-3-030-25820-7
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login