Prädiktive Modelle
Author:
Publisher
Springer Berlin Heidelberg
Link
https://link.springer.com/content/pdf/10.1007/978-3-662-67026-2_9
Reference40 articles.
1. Angelini, Giovanni; Angelis, Luca de (2019): Efficiency of online football betting markets. In: International Journal of Forecasting 35 (2), S. 712–721. DOI: https://doi.org/10.1016/j.ijforecast.2018.07.008.
2. Bernardo, Giovanni; Ruberti, Massimo; Verona, Roberto (2019): Semi-strong inefficiency in the fixed odds betting market: Underestimating the positive impact of head coach replacement in the main European soccer leagues. In: The Quarterly Review of Economics and Finance 71, S. 239–246. DOI: https://doi.org/10.1016/j.qref.2018.08.007.
3. Booth, Heather (2006): Demographic forecasting: 1980 to 2005 in review. In: International Journal of Forecasting 22 (3), S. 547–581. DOI: https://doi.org/10.1016/j.ijforecast.2006.04.001.
4. Boshnakov, Georgi; Kharrat, Tarak; McHale, Ian G. (2017): A bivariate Weibull count model for forecasting association football scores. In: International Journal of Forecasting 33 (2), S. 458–466. DOI: https://doi.org/10.1016/j.ijforecast.2016.11.006.
5. Brandt, Patrick T.; Freeman, John R.; Schrodt, Philip A. (2014): Evaluating forecasts of political conflict dynamics. In: International Journal of Forecasting 30 (4), S. 944–962. DOI: https://doi.org/10.1016/j.ijforecast.2014.03.014.
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