references.bib

@article{gu_approximated_2018,
	title = {Approximated adjusted fractional {Bayes} factors: {A} general method for testing informative hypotheses},
	volume = {71},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0007-1102, 2044-8317},
	shorttitle = {Approximated adjusted fractional {Bayes} factors},
	url = {https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/bmsp.12110},
	doi = {10.1111/bmsp.12110},
	abstract = {Informative hypotheses are increasingly being used in psychological sciences because they adequately capture researchers’ theories and expectations. In the Bayesian framework, the evaluation of informative hypotheses often makes use of default Bayes factors such as the fractional Bayes factor. This paper approximates and adjusts the fractional Bayes factor such that it can be used to evaluate informative hypotheses in general statistical models. In the fractional Bayes factor a fraction parameter must be specified which controls the amount of information in the data used for specifying an implicit prior. The remaining fraction is used for testing the informative hypotheses. We discuss different choices of this parameter and present a scheme for setting it. Furthermore, a software package is described which computes the approximated adjusted fractional Bayes factor. Using this software package, psychological researchers can evaluate informative hypotheses by means of Bayes factors in an easy manner. Two empirical examples are used to illustrate the procedure.},
	language = {en},
	number = {2},
	urldate = {2024-06-17},
	journal = {British Journal of Mathematical and Statistical Psychology},
	author = {Gu, Xin and Mulder, Joris and Hoijtink, Herbert},
	month = may,
	year = {2018},
	pages = {229--261},
	file = {Volltext:/Users/jonathan/Zotero/storage/DN82UQJT/Gu et al. - 2018 - Approximated adjusted fractional Bayes factors A .pdf:application/pdf},
}

@article{karimova_separating_2023,
	title = {Separating the wheat from the chaff: {Bayesian} regularization in dynamic social networks},
	volume = {74},
	issn = {03788733},
	shorttitle = {Separating the wheat from the chaff},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0378873323000217},
	doi = {10.1016/j.socnet.2023.02.006},
	language = {en},
	urldate = {2024-06-17},
	journal = {Social Networks},
	author = {Karimova, Diana and Leenders, Roger Th.A.J. and Meijerink-Bosman, Marlyne and Mulder, Joris},
	month = jul,
	year = {2023},
	pages = {139--155},
	file = {Volltext:/Users/jonathan/Zotero/storage/YMFDBUWP/Karimova et al. - 2023 - Separating the wheat from the chaff Bayesian regu.pdf:application/pdf},
}

@misc{statsbomb_statsbomb_2024,
	title = {{StatsBomb} {Open} {Data} {Repository}},
	url = {https://github.com/statsbomb},
	urldate = {2024-06-17},
	author = {{StatsBomb}},
	year = {2024},
	annote = {Accessed: 2024-06-17},
}

@article{conigliani_sensitivity_2000,
	title = {Sensitivity of the fractional {Bayes} factor to prior distributions},
	volume = {28},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0319-5724, 1708-945X},
	url = {https://onlinelibrary.wiley.com/doi/10.2307/3315983},
	doi = {10.2307/3315983},
	abstract = {Abstract
            The authors derive a measure of the sensitivity of the fractional Bayes factor, an index which is used to compare models when the priors for their respective parameters are improper, or when there is concern about robustness of the prior specification. They prove that in a large class of problems, this measure is a decreasing function of the fraction of the sample used to update the prior distribution before the models are compared.
          , 
            Les auteurs proposent une mesure de la sensibilité du facteur de Bayes fractionnaire, un indice de comparaison de modèles employé lorsque Ton s'inquiète de la robustesse des lois a priori sur les paramètres ou que celles‐ci sont impropres. IIs démontrent que dans beaucoup de situations, cette mesure décroǐt comme fonction de la fraction de l'échantillon utilisée pour mettre à jour les lois a priori avant de comparer les modèles.},
	language = {en},
	number = {2},
	urldate = {2024-06-17},
	journal = {Canadian Journal of Statistics},
	author = {Conigliani, Caterina and O'Hagan, Anthony},
	month = jun,
	year = {2000},
	pages = {343--352},
}

@article{van_de_schoot_bayesian_2014,
	title = {Bayesian analyses: {Where} to start and what to report},
	volume = {16},
	number = {2},
	journal = {European Health Psychologist},
	author = {Van De Schoot, Rens and Depaoli, Sarah},
	year = {2014},
	pages = {75--84},
}

@book{van_de_schoot_informative_2010,
	title = {Informative hypotheses: how to move beyond classical null hypothesis testing},
	publisher = {Utrecht University},
	author = {Van de Schoot, R and {others}},
	year = {2010},
}

@book{gu_bain_2023,
	title = {bain: {Bayes} {Factors} for {Informative} {Hypotheses}},
	url = {https://CRAN.R-project.org/package=bain},
	author = {Gu, Xin and Hoijtink, Herbert and Mulder, Joris and Lissa, Caspar J. van},
	year = {2023},
	annote = {R package version 0.2.10},
}

@article{ohagan_fractional_1995,
	title = {Fractional {Bayes} factors for model comparison},
	volume = {57},
	number = {1},
	journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
	author = {O'Hagan, Anthony},
	year = {1995},
	note = {Publisher: Wiley Online Library},
	pages = {99--118},
}

@misc{transfermarkt_vfb_2024,
	title = {{VfB} {Stuttgart} - {Club} {Profile}},
	url = {https://www.transfermarkt.com/vfb-stuttgart/startseite/verein/79},
	author = {{Transfermarkt}},
	year = {2024},
	annote = {[Accessed 18-Jun-2024]},
}