IDEAS home Printed from https://ideas.repec.org/a/spr/fuzodm/v23y2024i4d10.1007_s10700-024-09436-8.html
   My bibliography  Save this article

Music statistics: uncertain logistic regression models with applications in analyzing music

Author

Listed:
  • Jue Lu

    (Shaoxing University)

  • Lianlian Zhou

    (Shaoxing University)

  • Wenxing Zeng

    (Shaoxing University)

  • Anshui Li

    (Shaoxing University)

Abstract

In the realm of data analysis, traditional statistical methods often struggle when faced with ambiguity and uncertainty inherent in real world data. Uncerainty theory, developed to better model and interpret such data, offers a promising alternative to conventional techniques. In this paper, we establish logistic regression models to initiate music statistics based on uncertainty theory. In particular, we will classify the music into different types named Baroque, Classical, Romantic, and Impressionism based on four characteristics: harmonic complexity, rhythmic complexity, texture complexity, and formal structure, with the help of the uncertain logistic models proposed. This theoretical framework for music classification provides a nuanced understanding of how music is interpreted under conditions of ambiguity and variability. Compared with the probabilistic counterpart, our approach highlights the versatility of uncertainty theory and provides researchers one much more feasible method to analyze the often-subjective nature of music reception, as well as broadening the potential applications of uncertainty theory.

Suggested Citation

  • Jue Lu & Lianlian Zhou & Wenxing Zeng & Anshui Li, 2024. "Music statistics: uncertain logistic regression models with applications in analyzing music," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 637-654, December.
    • Handle: RePEc:spr:fuzodm:v:23:y:2024:i:4:d:10.1007_s10700-024-09436-8
    DOI: 10.1007/s10700-024-09436-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10700-024-09436-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10700-024-09436-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    2. Xiangfeng Yang & Baoding Liu, 2019. "Uncertain time series analysis with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 263-278, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    2. Jinran Chen, 2024. "Prediction of global trade network evolution with uncertain multi-step time series forecasting method," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 387-414, September.
    3. Zhongfeng Qin & Qiqi Li, 2023. "An uncertain support vector machine with imprecise observations," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 611-629, December.
    4. Waichon Lio & Rui Kang, 2023. "Bayesian rule in the framework of uncertainty theory," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 337-358, September.
    5. Yang Liu & Zhongfeng Qin & Xiang Li, 2024. "Are the queueing systems in practice random or uncertain? Evidence from online car-hailing data in Beijing," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 497-511, December.
    6. Tingqing Ye & Xiangfeng Yang, 2021. "Analysis and prediction of confirmed COVID-19 cases in China with uncertain time series," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 209-228, June.
    7. Tingting Yang & Xiaoxia Huang, 2022. "A New Portfolio Optimization Model Under Tracking-Error Constraint with Linear Uncertainty Distributions," Journal of Optimization Theory and Applications, Springer, vol. 195(2), pages 723-747, November.
    8. Liu, Z. & Yang, Y., 2021. "Pharmacokinetic model based on multifactor uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    9. Jian Zhou & Yujiao Jiang & Athanasios A. Pantelous & Weiwen Dai, 2023. "A systematic review of uncertainty theory with the use of scientometrical method," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 463-518, September.
    10. Chen, Wen-Bin & Li, Xiao-Yang & Wu, Ji-Peng & Kang, Rui, 2024. "Uncertain random accelerated degradation modelling and statistical analysis with aleatory and epistemic uncertainties from multiple dimensions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    11. Tingqing Ye & Baoding Liu, 2022. "Uncertain hypothesis test with application to uncertain regression analysis," Fuzzy Optimization and Decision Making, Springer, vol. 21(2), pages 157-174, June.
    12. Xiaoxia Huang & Xue Meng & Xiaozhu Xu, 2024. "Portfolio selection with second order uncertain dominance constraint," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 561-575, December.
    13. Zhe Liu, 2021. "Uncertain growth model for the cumulative number of COVID-19 infections in China," Fuzzy Optimization and Decision Making, Springer, vol. 20(2), pages 229-242, June.
    14. Liu He & Yuanguo Zhu & Yajing Gu, 2023. "Nonparametric estimation for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 697-715, December.
    15. Gholamreza Hesamian & Arne Johannssen & Nataliya Chukhrova, 2023. "A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data," Mathematics, MDPI, vol. 11(13), pages 1-17, June.
    16. Xiangfeng Yang & Hua Ke, 2023. "Uncertain interest rate model for Shanghai interbank offered rate and pricing of American swaption," Fuzzy Optimization and Decision Making, Springer, vol. 22(3), pages 447-462, September.
    17. Weiwei Wang & Dan A. Ralescu & Panpan Zhang, 2024. "Valuation of convertible bond based on uncertain fractional differential equation," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 513-538, December.
    18. Li, Bo & Lu, Ziqiang, 2023. "Uncertain random enhanced index tracking for portfolio selection with parameter estimation and hypothesis test," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    19. He, Liu & Zhu, Yuanguo, 2024. "Nonparametric estimation for uncertain fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:fuzodm:v:23:y:2024:i:4:d:10.1007_s10700-024-09436-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.