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Partial monotonicity of entropy measures


  • Shangari, Dhruv
  • Chen, Jiahua


The quantification of entropy has prominence in a diverse range of fields of study including information theory, quantum mechanics, thermodynamics, ecology, evolutionary biology and even sociology. Suppose we interpret the entropy of a random object as a measurement of the uncertainty about its outcome. This measurement is expected to decrease when the object’s outcome is confined into a shrinking interval. Entropies conforming to this intuition are thus sensible and likely useful measures of uncertainty. In this paper, we give a necessary and sufficient condition for the Shannon entropy of an absolutely continuous random variable to be an increasing function of the interval. Similar results are also obtained for the Renyi entropy of absolutely continuous random variables and their convolution.

Suggested Citation

  • Shangari, Dhruv & Chen, Jiahua, 2012. "Partial monotonicity of entropy measures," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1935-1940.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:11:p:1935-1940 DOI: 10.1016/j.spl.2012.06.029

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    References listed on IDEAS

    1. Mark Bagnoli & Ted Bergstrom, 2005. "Log-concave probability and its applications," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 26(2), pages 445-469, August.
    2. Chen, Jiahua & van Eeden, Constance & Zidek, James, 2010. "Uncertainty and the conditional variance," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1764-1770, December.
    3. Burdett, Kenneth, 1996. "Truncated means and variances," Economics Letters, Elsevier, vol. 52(3), pages 263-267, September.
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    Cited by:

    1. Gupta, Nitin & Bajaj, Rakesh Kumar, 2013. "On partial monotonic behaviour of some entropy measures," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1330-1338.


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