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Log-Cumulant Parameter Estimator of Log-Normal Distribution

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

Based on the second-kind statistics, the log-cumulant estimator is proposed for parameter estimation of the log-normal distribution. The estimates of the shape parameter and scale parameter are independent for the log-cumulant estimator. Parameter estimation results from Monte Carlo simulation demonstrate that, the log-cumulant estimator is insensitive to samples and it leads to better performance when compared to the moment estimator.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Xiamen, China

    Zengguo Sun & Jixiang Du

Authors
  1. Zengguo Sun
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  2. Jixiang Du
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Sun, Z., Du, J. (2014). Log-Cumulant Parameter Estimator of Log-Normal Distribution. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_72

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_72

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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