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Mathematical and statistical methods...

MAF 2024 ((2024 :)

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Mathematical and statistical methods for actuarial sciences and finance = MAF2024 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical and statistical methods for actuarial sciences and finance/ edited by Marco Corazza ... [et al.].
Reminder of title:	MAF2024 /
remainder title:	MAF2024
other author:	Corazza, Marco.
corporate name:	MAF 2024
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xi, 304 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Insurance - Congresses. - Mathematical models -
Online resource:	https://doi.org/10.1007/978-3-031-64273-9
ISBN:	9783031642739

Mathematical and statistical methods for actuarial sciences and finance = MAF2024 /
Mathematical and statistical methods for actuarial sciences and financeMAF2024 /[electronic resource] :MAF2024edited by Marco Corazza ... [et al.]. - Cham :Springer Nature Switzerland :2024. - xi, 304 p. :ill., digital ;24 cm.

The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance - MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference. The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca' Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024) This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF. The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.

ISBN: 9783031642739

Standard No.: 10.1007/978-3-031-64273-9doiSubjects--Topical Terms:

3270877
Insurance
--Mathematical models--Congresses.

LC Class. No.: HG8017 / .M34 2024

Dewey Class. No.: 368.01

Mathematical and statistical methods for actuarial sciences and finance = MAF2024 /
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300 $a xi, 304 p. : $b ill., digital ; $c 24 cm.
520 $a The book presents a collection of peer-reviewed short papers selected from those presented at the International Conference Mathematical and Statistical Methods for Actuarial Sciences and Finance - MAF2024. The year 2024 marks the twentieth anniversary of the first edition of this conference. The idea behind this book is that collaboration and cross-pollination between mathematicians and statisticians working in actuarial sciences and finance could enhance research in these fields. The effectiveness of this concept has been demonstrated by widespread participation across all editions, held at various locations including the University of Salerno, Italy (2004, 2006, 2010, 2014, and 2022); Ca' Foscari University of Venice, Italy (2008, 2012, and 2020); University Paris-Dauphine in Paris, France (2016); University Carlos III of Madrid, Madrid (2018); and University of Le Havre Normandie, Le Havre, France (2024) This effectiveness is also evident in the attention consistently shown by both the scientific community and professionals toward the volumes of peer-reviewed papers accompanying all past editions of MAF. The book is a valuable resource for academics, researchers, Ph.D. students, and professionals. Furthermore, it is also of interest to other readers with a quantitative background.
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650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
700 1 $a Corazza, Marco. $3 2089089
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