Computational Statistics

, Volume 32, Issue 4, pp 1339–1355 | Cite as

On moment-type estimators for a class of log-symmetric distributions

  • N. Balakrishnan
  • Helton Saulo
  • Marcelo Bourguignon
  • Xiaojun Zhu
Original Paper
  • 126 Downloads

Abstract

In this paper, we propose three simple closed form estimators for a class of log-symmetric distributions on \({\mathbb {R}}^{+}\). The proposed methods make use of some key properties of this class of distributions. We derive the asymptotic distributions of these estimators. The performance of the proposed estimators are then compared with those of the maximum likelihood estimators through Monte Carlo simulations. Finally, some illustrative examples are presented to illustrate the methods of estimation developed here.

Keywords

Asymptotic normality Hodges–Lehmann estimator Log-symmetric distributions Maximum likelihood estimator Moment estimator Modified moment estimator 

Notes

Acknowledgements

We thank the associate editor and the reviewer for the constructive comments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • N. Balakrishnan
    • 1
  • Helton Saulo
    • 1
    • 2
  • Marcelo Bourguignon
    • 3
  • Xiaojun Zhu
    • 4
  1. 1.Department of Mathematics and StatisticsMcMaster UniversityHamiltonCanada
  2. 2.Departamento de EstatísticaUniversidade de BrasíliaBrasíliaBrazil
  3. 3.Departamento de EstatísticaUniversidade Federal do Rio Grande do NorteNatalBrazil
  4. 4.Department of Mathematical SciencesXi’an Jiaotong-Liverpool UniversitySuzhouPeople’s Republic of China

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