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Derive power law distribution with maximum Deng entropy

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  • Yu, Zihan
  • Deng, Yong

Abstract

As one of the typical distributions, power law distribution is widely found in natural world. However, how to derive power law is still an open issue. The main contribution of this paper is to propose a method to derive power law distribution with maximum Deng entropy. In the proposed method, Lagrange multiplier approach, combined with the constraint of two given conditions, is used to obtain power law distribution based on maximum Deng entropy. Some numerical examples are used to illustrate the properties of the distribution.

Suggested Citation

  • Yu, Zihan & Deng, Yong, 2022. "Derive power law distribution with maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
  • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p2:s0960077922010566
    DOI: 10.1016/j.chaos.2022.112877
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    References listed on IDEAS

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    1. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    2. Chenhui Qiang & Yong Deng & Kang Hao Cheong, 2022. "Information Fractal Dimension Of Mass Function," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(06), pages 1-12, September.
    3. Wang, Hongping & Fang, Yi-Ping & Zio, Enrico, 2022. "Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    4. Levy, Moshe & Solomon, Sorin, 1997. "New evidence for the power-law distribution of wealth," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 242(1), pages 90-94.
    5. Xingyuan Chen & Yong Deng, 2022. "An Evidential Software Risk Evaluation Model," Mathematics, MDPI, vol. 10(13), pages 1-19, July.
    6. Hanel, Rudolf & Thurner, Stefan, 2005. "Derivation of power-law distributions within standard statistical mechanics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 351(2), pages 260-268.
    7. Narayanaswamy Balakrishnan & Francesco Buono & Maria Longobardi, 2022. "On Cumulative Entropies in Terms of Moments of Order Statistics," Methodology and Computing in Applied Probability, Springer, vol. 24(1), pages 345-359, March.
    8. Balakrishnan, Narayanaswamy & Buono, Francesco & Longobardi, Maria, 2022. "A unified formulation of entropy and its application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Brechtl, Jamieson & Xie, Xie & Liaw, Peter K. & Zinkle, Steven J., 2018. "Complexity modeling and analysis of chaos and other fluctuating phenomena," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 166-175.
    10. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    11. Muir, Callum & Cortez, Jordan & Grigolini, Paolo, 2020. "Interacting faults in california and hindu kush," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    12. Balakrishnan, Narayanaswamy & Buono, Francesco & Longobardi, Maria, 2022. "On Tsallis extropy with an application to pattern recognition," Statistics & Probability Letters, Elsevier, vol. 180(C).
    13. Contreras-Reyes, Javier E., 2021. "Lerch distribution based on maximum nonsymmetric entropy principle: Application to Conway’s game of life cellular automaton," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
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