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Two-dimensional Regression Modelling with Copula Dependencies and a Focus on Count Data and Sports Applications (English shop)
Hendrik van der Wurp (Author)Preview
Extract, PDF (380 KB)
Table of Contents, PDF (93 KB)
| ISBN-13 (Hard Copy) | 9783736979253 |
| ISBN-13 (eBook) | 9783736969254 |
| Language | English |
| Page Number | 116 |
| Lamination of Cover | glossy |
| Edition | 1. |
| Publication Place | Göttingen |
| Place of Dissertation | Dortmund |
| Publication Date | 2023-12-12 |
| General Categorization | Dissertation |
| Departments |
Statistics and operations research, business mathematics
|
| Keywords | Statistics, Statistical Modelling, Regression, LASSO, Regression Modelling, Copula, Football, Association Football, Dependencies, Football Results, Competitive Settings, Machine Learning, Regularisation, Feature Selection, FIFA World Cups, Joint Modelling, Count Data, Sports Applications, Betting, Bundesliga, Soccer, Primera Division, Data Analysis, Bivariate Response, Cross Validation, Benchmarking, Model Validation, Forecasting, Correlation, Approximation, Statistik, Statistische Modellierung, Regression, Fußball, Abhängigkeiten, Variablenauswahl, FIFA Weltmeisterschaften, Pönalisierung, Fußballergebnisse, Kompetitive Szenarien, Maschinelles Lernen, Zähldaten, Sportanwendungen, Wetten |
Description
This work was fundamentally motivated by the application of regression modelling to football match outcomes and specifically FIFA World Cups. Modelling football results is a rather popular topic. However, there is no gold standard on how to tackle the bivariate nature of match outcomes and how to assess the possible underlying dependency thereof. This work proposes to use the mathematical structure of copulas to include the bivariate dependency structure within the modelling process. In this context, two regularization approaches were implemented into existing infrastructure and multiple showcases of the methodology are presented.
