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Some Characterization Results on Dynamic Cumulative Residual Tsallis Entropy

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  • Madan Mohan Sati
  • Nitin Gupta

Abstract

We propose a generalized cumulative residual information measure based on Tsallis entropy and its dynamic version. We study the characterizations of the proposed information measure and define new classes of life distributions based on this measure. Some applications are provided in relation to weighted and equilibrium probability models. Finally the empirical cumulative Tsallis entropy is proposed to estimate the new information measure.

Suggested Citation

  • Madan Mohan Sati & Nitin Gupta, 2015. "Some Characterization Results on Dynamic Cumulative Residual Tsallis Entropy," Journal of Probability and Statistics, Hindawi, vol. 2015, pages 1-8, October.
    • Handle: RePEc:hin:jnljps:694203
    DOI: 10.1155/2015/694203
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    Cited by:

    1. 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).
    2. Mohamed S. Mohamed & Haroon M. Barakat & Salem A. Alyami & Mohamed A. Abd Elgawad, 2022. "Cumulative Residual Tsallis Entropy-Based Test of Uniformity and Some New Findings," Mathematics, MDPI, vol. 10(5), pages 1-14, February.
    3. G. Rajesh & S. M. Sunoj, 2019. "Some properties of cumulative Tsallis entropy of order $$\alpha $$ α," Statistical Papers, Springer, vol. 60(3), pages 933-943, June.
    4. Mao, Xuegeng & Shang, Pengjian & Wang, Jianing & Yin, Yi, 2020. "Fractional cumulative residual Kullback-Leibler information based on Tsallis entropy," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. Mohamed Said Mohamed, 2020. "On Cumulative Tsallis Entropy and Its Dynamic Past Version," Indian Journal of Pure and Applied Mathematics, Springer, vol. 51(4), pages 1903-1917, December.

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