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Two-dimensional Regression Modelling with Copula Dependencies and a Focus on Count Data and Sports Applications (Tienda española)
Hendrik van der Wurp (Autor)Previo
Lectura de prueba, PDF (380 KB)
Indice, PDF (93 KB)
| ISBN-13 (Impresion) | 9783736979253 |
| ISBN-13 (E-Book) | 9783736969254 |
| Idioma | Inglés |
| Numero de paginas | 116 |
| Laminacion de la cubierta | Brillante |
| Edicion | 1. |
| Lugar de publicacion | Göttingen |
| Lugar de la disertacion | Dortmund |
| Fecha de publicacion | 12.12.2023 |
| Clasificacion simple | Tesis doctoral |
| Area |
Estadísticas y operación de investigaciones, Matemática para negocios
|
| Palabras claves | 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 |
Descripcion
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.
