References
- H. Akaike. Information Theory as an Extension of the Maximum Likelihood Principle, Second international symposium on information theory. Petrov, Boris Nikolaevich and Csaki, F, 1973, pp. 267–281.
- W. Berger. Why is this song popular? (feat spotify). Available at https://medium.com/@albert.w.berger/what-makes-a-song-popular-in-a-certain-country-
- W.H. Bonat, P.J. Ribeiro, and W.M. Zeviani, Likelihood analysis for a class of beta mixed models, J. Appl. Stat. 42 (Aug 2014), pp. 252–266. https://doi.org/https://doi.org/10.1080/02664763.2014.947248.
- M.E. Brooks, K. Kristensen, K.J. van Benthem, A. Magnusson, C.W. Berg, A. Nielsen, H.J. Skaug, M. Maechler, and B.M. Bolker, glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling, R. J. 9 (2017), pp. 378–400. Available at https://journal.r-project.org/archive/2017/RJ-2017-066/index.html. doi: https://doi.org/10.32614/RJ-2017-066
- Charlie, Rcharlie web site. Available at https://www.rcharlie.com//, 2019.
- Z. Chen and D.B. Dunson, Random effects selection in linear mixed models, Biometrics 59 (2003), pp. 762–769. doi: https://doi.org/10.1111/j.0006-341X.2003.00089.x
- R. Dhanaraj and B. Logan, Automatic prediction of hit songs, in ISMIR 2005, 6th International Conference on Music Information Retrieval, 11–15 September 2005, Proceedings, HP Laboratories Cambridge, London, UK, 2005, pp. 488–491. Available at http://ismir2005.ismir.net/proceedings/2024.pdf
- S. Ferrari and F. Cribari-Neto, Beta regression for modelling rates and proportions, J. Appl. Stat. 31 (2004), pp. 799–815. https://doi.org/https://doi.org/10.1080/0266476042000214501.
- D.E. Giles, Superstardom in the us popular music industry revisited, Econ. Lett. 92 (2006), pp. 68–74. https://doi.org/https://doi.org/10.1016/j.econlet.2006.01.022.
- S. Greven and T. Kneib, On the behaviour of marginal and conditional AIC in linear mixed models, Biometrika 97 (2010), pp. 773–789. https://doi.org/https://doi.org/10.1093/biomet/asq042.
- M. Hunger, A. Dring, and R. Holle, Longitudinal beta regression models for analyzing health-related quality of life scores over time, BMC Med. Res. Methodol. 12 (2012). doi: https://doi.org/10.1186/1471-2288-12-144
- S.K. Kinney and D.B. Dunson, Fixed and random effects selection in linear and logistic models, Biometrics 63 (2007), pp. 690–698. doi: https://doi.org/10.1111/j.1541-0420.2007.00771.x
- J. Lee and J.-S. Lee, Music popularity: metrics, characteristics, and audio-based prediction, IEEE Trans. Multimedia 20 (Nov 2018), pp. 3173–3182. https://doi.org/https://doi.org/10.1109/TMM.2018.2820903.
- A. Lerch, The Relation Between Music Technology and Music Industry, Springer, Berlin Heidelberg, 2018, pp. 899–909. https://doi.org/https://doi.org/10.1007/978-3-662-55004-5_44.
- G. Lovison, M. Sciandra, A. Tomasello, and S. Calvo, Modeling posidonia oceanica growth data: from linear to generalized linear mixed models, Environmetrics 22 (2011), pp. 370–382. Available at https://onlinelibrary.wiley.com/doi/abs/https://doi.org/10.1002/env.1063.
- K. Middlebrook and K. Sheik, Song hit prediction: predicting billboard hits using spotify data, 2019.
- M. Nasreldin, Song popularity predictor. Available at https://towardsdatascience.com/song-popularity-predictor-1ef69735e380, 2018.
- Y. Ni, R. Santos-rodrguez, M. Mcvicar, and T.D. Bie, Hit song science once again a science? 2015.
- R. Nijkamp, Prediction of product success: explaining song popularity by audio features from spotify data, July 2018. Available at http://essay.utwente.nl/75422/.
- F. Pachet, Musical metadata and knowledge management, in Encyclopedia of Knowledge Management, 2nd ed., D. Schwartz, and D. Te'eni, eds., IGI Global, Sony CSL, Paris, France, 2011, pp. 1192–1199.
- M. Poggini, Liga. La biogra a. BUR Biblioteca Univ. Rizzoli, ITALIA, 2010. ISBN 10: 8817040053.
- M. Sciandra and A. Plaia, A graphical model selection tool for mixed models, Comm. Stat. Simul. Comput. 47 (2018), pp. 2624–2638. https://doi.org/https://doi.org/10.1080/03610918.2017.1353617.
- J.A. Sloboda, Music in everyday life, the role of emotions, in Handbook of Music and Emotion: Theory, Research, Applications, P. N. Juslin and J. Sloboda, eds., Oxford University Press, Oxford, 2011, pp. 1–37.
- SpotifyWebAPI, Spotify for developers, 2019. Available at https://open.spotify.com/.
- F. Vaida and S. Blanchard, Conditional Akaike information for mixed-effects models, Biometrika 92 (2005), pp. 351–370. https://doi.org/https://doi.org/10.1093/biomet/92.2.351.