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Counting statistics for dependent ra...

Bernardi, Enrico.

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Counting statistics for dependent random events = with a focus on finance /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Counting statistics for dependent random events/ by Enrico Bernardi, Silvia Romagnoli.
Reminder of title:	with a focus on finance /
Author:	Bernardi, Enrico.
other author:	Romagnoli, Silvia.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiii, 206 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- I The Main Ingredients -- 1 Clustering -- 2 Copula Function and C-volume -- 3 Combinatorics and Random Matrices: A Brief Review -- II Mixing the Ingredients: A Recipe for a New Aggregation Algorithm -- 4 Counting a Random Event: Traditional Approach and New Perspectives -- 5 A New Copula-based Approach for Counting: The Distorted and the Limiting Case -- 6 Real Data Empirical Applications.
Contained By:	Springer Nature eBook
Subject:	Copulas (Mathematical statistics) -
Online resource:	https://doi.org/10.1007/978-3-030-64250-1
ISBN:	9783030642501

Counting statistics for dependent random events = with a focus on finance /
Bernardi, Enrico.

Counting statistics for dependent random eventswith a focus on finance /[electronic resource] :by Enrico Bernardi, Silvia Romagnoli. - Cham :Springer International Publishing :2021. - xiii, 206 p. :ill., digital ;24 cm.

Preface -- I The Main Ingredients -- 1 Clustering -- 2 Copula Function and C-volume -- 3 Combinatorics and Random Matrices: A Brief Review -- II Mixing the Ingredients: A Recipe for a New Aggregation Algorithm -- 4 Counting a Random Event: Traditional Approach and New Perspectives -- 5 A New Copula-based Approach for Counting: The Distorted and the Limiting Case -- 6 Real Data Empirical Applications.

This book on counting statistics presents a novel copula-based approach to counting dependent random events. It combines clustering, combinatorics-based algorithms and dependence structure in order to tackle and simplify complex problems, without disregarding the hierarchy of or interconnections between the relevant variables. These problems typically arise in real-world applications and computations involving big data in finance, insurance and banking, where experts are confronted with counting variables in monitoring random events. In this new approach, combinatorial distributions of random events are the core element. In order to deal with the high-dimensional features of the problem, the combinatorial techniques are used together with a clustering approach, where groups of variables sharing common characteristics and similarities are identified and the dependence structure within groups is taken into account. The original problems can then be modeled using new classes of copulas, referred to here as clusterized copulas, which are essentially based on preliminary groupings of variables depending on suitable characteristics and hierarchical aspects. The book includes examples and real-world data applications, with a special focus on financial applications, where the new algorithms' performance is compared to alternative approaches and further analyzed. Given its scope, the book will be of interest to master students, PhD students and researchers whose work involves or can benefit from the innovative methodologies put forward here. It will also stimulate the empirical use of new approaches among professionals and practitioners in finance, insurance and banking.

ISBN: 9783030642501

Standard No.: 10.1007/978-3-030-64250-1doiSubjects--Topical Terms:

1086625
Copulas (Mathematical statistics)

LC Class. No.: QA273.6 / .B476 2021

Dewey Class. No.: 519.535

Counting statistics for dependent random events = with a focus on finance /
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100 1 $a Bernardi, Enrico. $3 3493215
245 1 0 $a Counting statistics for dependent random events $h [electronic resource] : $b with a focus on finance / $c by Enrico Bernardi, Silvia Romagnoli.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiii, 206 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- I The Main Ingredients -- 1 Clustering -- 2 Copula Function and C-volume -- 3 Combinatorics and Random Matrices: A Brief Review -- II Mixing the Ingredients: A Recipe for a New Aggregation Algorithm -- 4 Counting a Random Event: Traditional Approach and New Perspectives -- 5 A New Copula-based Approach for Counting: The Distorted and the Limiting Case -- 6 Real Data Empirical Applications.
520 $a This book on counting statistics presents a novel copula-based approach to counting dependent random events. It combines clustering, combinatorics-based algorithms and dependence structure in order to tackle and simplify complex problems, without disregarding the hierarchy of or interconnections between the relevant variables. These problems typically arise in real-world applications and computations involving big data in finance, insurance and banking, where experts are confronted with counting variables in monitoring random events. In this new approach, combinatorial distributions of random events are the core element. In order to deal with the high-dimensional features of the problem, the combinatorial techniques are used together with a clustering approach, where groups of variables sharing common characteristics and similarities are identified and the dependence structure within groups is taken into account. The original problems can then be modeled using new classes of copulas, referred to here as clusterized copulas, which are essentially based on preliminary groupings of variables depending on suitable characteristics and hierarchical aspects. The book includes examples and real-world data applications, with a special focus on financial applications, where the new algorithms' performance is compared to alternative approaches and further analyzed. Given its scope, the book will be of interest to master students, PhD students and researchers whose work involves or can benefit from the innovative methodologies put forward here. It will also stimulate the empirical use of new approaches among professionals and practitioners in finance, insurance and banking.
650 0 $a Copulas (Mathematical statistics) $3 1086625
650 0 $a Dependence (Statistics) $3 1245252
650 0 $a Finance $x Mathematical models. $3 578740
650 1 4 $a Statistics for Business, Management, Economics, Finance, Insurance. $3 3382132
650 2 4 $a Applications of Mathematics. $3 890893
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Financial Engineering. $3 2191344
650 2 4 $a Economic Theory/Quantitative Economics/Mathematical Methods. $3 2162305
700 1 $a Romagnoli, Silvia. $3 3493216
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-64250-1
950 $a Mathematics and Statistics (SpringerNature-11649)