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International Conference of the Thailand Econometrics Society

TES 2019: Structural Changes and their Econometric Modeling pp 352–363Cite as

Confidence Intervals for Coefficient of Variation of Three Parameters Delta-Lognormal Distribution

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 808))

Abstract

The aim of this paper is to propose confidence intervals using the concepts that include the generalized fiducial interval (GFI) and the method of variance estimates recovery (MOVER). The performance of the proposed approaches were gauged in terms of the coverage probabilities and the expected lengths. Simulation studies shown that GFI outperformed other approaches with small sample sizes together with small variances. For larger sample sizes, GFI and MOVER based on the Jeffreys performed better than the other approaches when the variances were large. Furthermore, the results for the cases of high proportion of non-zero values of large sample sizes indicated that GFI is suited for small variance, and if variances are growing, then MOVER based on the Wilson score should be chosen.

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Acknowledgements

This research was funded by King Mongkut’s University of Technology North Bangkok. Grant number: KMUTNB-61-PHD-004.

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Authors and Affiliations

  1. Faculty of Applied Science, Department of Applied Statistics, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Noppadon Yosboonruang, Suparat Niwitpong & Sa-Aat Niwitpong

Authors
  1. Noppadon Yosboonruang
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  2. Suparat Niwitpong
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  3. Sa-Aat Niwitpong
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Correspondence to Sa-Aat Niwitpong .

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Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

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Yosboonruang, N., Niwitpong, S., Niwitpong, SA. (2019). Confidence Intervals for Coefficient of Variation of Three Parameters Delta-Lognormal Distribution. In: Kreinovich, V., Sriboonchitta, S. (eds) Structural Changes and their Econometric Modeling. TES 2019. Studies in Computational Intelligence, vol 808. Springer, Cham. https://doi.org/10.1007/978-3-030-04263-9_27

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