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Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions

Author

Listed:
  • Warisa Thangjai

    (Department of Statistics, Faculty of Science, Ramkhamhaeng University, Bangkok 10240, Thailand
    These authors contributed equally to this work.)

  • Sa-Aat Niwitpong

    (Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
    These authors contributed equally to this work.)

Abstract

In this article, we propose approaches for constructing confidence intervals for the single signal-to-noise ratio (SNR) of a log-normal distribution and the difference in the SNRs of two log-normal distributions. The performances of all of the approaches were compared, in terms of the coverage probability and average length, using Monte Carlo simulations for varying values of the SNRs and sample sizes. The simulation studies demonstrate that the generalized confidence interval (GCI) approach performed well, in terms of coverage probability and average length. As a result, the GCI approach is recommended for the confidence interval estimation for the SNR and the difference in SNRs of two log-normal distributions.

Suggested Citation

  • Warisa Thangjai & Sa-Aat Niwitpong, 2019. "Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions," Stats, MDPI, vol. 2(1), pages 1-10, February.
  • Handle: RePEc:gam:jstats:v:2:y:2019:i:1:p:12-173:d:209628
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    References listed on IDEAS

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    1. Ahmed A. Soliman & A. H. Abd Ellah & N. A. Abou-Elheggag & A. A. Modhesh, 2012. "Estimation of the coefficient of variation for non-normal model using progressive first-failure-censoring data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2741-2758, August.
    2. Ahmed N. Albatineh & Ibrahimou Boubakari & B. M. Golam Kibria, 2017. "New confidence interval estimator of the signal-to-noise ratio based on asymptotic sampling distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(2), pages 574-590, January.
    3. H. E.T. Holgersson & Peter S. Karlsson & Rashid Mansoor, 2012. "Estimating mean-standard deviation ratios of financial data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 657-671, August.
    4. Zou, Guang Yong & Taleban, Julia & Huo, Cindy Y., 2009. "Confidence interval estimation for lognormal data with application to health economics," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3755-3764, September.
    5. Florence George & B. M. Golam Kibria, 2012. "Confidence intervals for estimating the population signal-to-noise ratio: a simulation study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1225-1240, November.
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    Cited by:

    1. Weizhong Tian & Yaoting Yang & Tingting Tong, 2022. "Confidence Intervals Based on the Difference of Medians for Independent Log-Normal Distributions," Mathematics, MDPI, vol. 10(16), pages 1-14, August.

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