Búsqueda

Selecting bivariate copula models using image recognition

Selecting bivariate copula models using image recognition
Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20220026086
003  MAP
005  20221003153455.0
008  221003e20220905bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20130016566‎$a‎Tsanakas, Andreas
24510‎$a‎Selecting bivariate copula models using image recognition‎$c‎Andreas Tsanakas, Rui Zhu
520  ‎$a‎The choice of a copula model from limited data is a hard but important task. Motivated by the visual patterns that different copula models produce in smoothed density heatmaps, we consider copula model selection as an image recognition problem. We extract image features from heatmaps using the pre-trained AlexNet and present workflows for model selection that combine image features with statistical information. We employ dimension reduction via Principal Component and Linear Discriminant Analyses and use a Support Vector Machine classifier. Simulation studies show that the use of image data improves the accuracy of the copula model selection task, particularly in scenarios where sample sizes and correlations are low. This finding indicates that transfer learning can support statistical procedures of model selection. We demonstrate application of the proposed approach to the joint modelling of weekly returns of the MSCI and RISX indices.
540  ‎$a‎La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"‎$f‎‎$u‎https://creativecommons.org/licenses/by/4.0‎$9‎43
650 4‎$0‎MAPA20080579258‎$a‎Cálculo actuarial
650 4‎$0‎MAPA20080559373‎$a‎Matemáticas
650 4‎$0‎MAPA20090035034‎$a‎Modelización mediante cópulas
7001 ‎$0‎MAPA20220008549‎$a‎Zhu, Rui
7730 ‎$w‎MAP20077000420‎$g‎05/09/2022 Volumen 52 Número 3 - septiembre 2022 , p. 707-734‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association
856  ‎$q‎application/pdf‎$w‎1116866‎$y‎Recurso electrónico / Electronic resource