LEADER 04398nam 22007335 450 001 9910483919303321 005 20250505003805.0 010 $a3-030-64250-X 024 7 $a10.1007/978-3-030-64250-1 035 $a(CKB)4100000011807096 035 $a(MiAaPQ)EBC6524975 035 $a(Au-PeEL)EBL6524975 035 $a(OCoLC)1243539452 035 $a(PPN)254725686 035 $a(DE-He213)978-3-030-64250-1 035 $a(EXLCZ)994100000011807096 100 $a20210322d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aCounting Statistics for Dependent Random Events $eWith a Focus on Finance /$fby Enrico Bernardi, Silvia Romagnoli 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (213 pages) $cillustrations 311 08$a3-030-64249-6 320 $aIncludes bibliographical references. 327 $aPreface -- 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. 330 $aThis 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. 606 $aStatistics 606 $aMathematics 606 $aStatistics 606 $aProbabilities 606 $aFinancial engineering 606 $aEconometrics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aApplications of Mathematics 606 $aStatistical Theory and Methods 606 $aProbability Theory 606 $aFinancial Engineering 606 $aQuantitative Economics 615 0$aStatistics. 615 0$aMathematics. 615 0$aStatistics. 615 0$aProbabilities. 615 0$aFinancial engineering. 615 0$aEconometrics. 615 14$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aApplications of Mathematics. 615 24$aStatistical Theory and Methods. 615 24$aProbability Theory. 615 24$aFinancial Engineering. 615 24$aQuantitative Economics. 676 $a519.535 700 $aBernardi$b Enrico$f1838-1900,$0851973 702 $aRomagnoli$b Silvia 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483919303321 996 $aCounting statistics for dependent random events$91902307 997 $aUNINA