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Estimation of Shape Parameter of GGD Function by Negentropy Matching

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Abstract

In this paper we present a novel method for the estimation of the shape parameter of the Generalized Gaussian Distribution (GGD) function for the leptokurtic and Gaussian signals by matching negentropy of GGD function and that of data approximated by some non-polynomial functions. The negentropy of GGD function is monotonic function of its shape parameter for values corresponding to super-Gaussian and Gaussian distribution family. The simulation results have been compared with those obtained by existing methods such as Mallat’s method and Kurtosis matching method. It has been found that the proposed method is effective and useful in the cases where we have a few observation samples and distribution is highly spiky.

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Authors and Affiliations

  1. Nara Institute of Science and Technology, Takayama-cho, Nara, 6300101, Japan

    Rajkishore Prasad, Hiroshi Saruwatari & Kiyohiro Shikano

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  1. Rajkishore Prasad
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  2. Hiroshi Saruwatari
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  3. Kiyohiro Shikano
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Correspondence to Rajkishore Prasad.

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Prasad, R., Saruwatari, H. & Shikano, K. Estimation of Shape Parameter of GGD Function by Negentropy Matching. Neural Process Lett 22, 377–389 (2005). https://doi.org/10.1007/s11063-005-1385-9

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