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Confidence intervals for estimating the population signal-to-noise ratio: a simulation study

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  • Florence George
  • B. M. Golam Kibria

Abstract

This paper considered several confidence intervals for estimating the population signal-to-noise ratio based on parametric, non-parametric and modified methods. A simulation study has been conducted to compare the performance of the interval estimators under both symmetric and skewed distributions. We reported coverage probability and average width of the interval estimators. Based on the simulation study, we observed that some of our proposed interval estimators are performing better in the sense of smaller width and coverage probability and have been recommended for the researchers.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1225-1240
    DOI: 10.1080/02664763.2011.644527
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    Cited by:

    1. George Djolov, 2014. "A Note on the Estimation of the Gini Index," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(3), pages 237-256, August.
    2. 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.
    3. Abdelmalek Kouadri & Karim Baiche & Mimoun Zelmat, 2014. "Blind source separation filters-based-fault detection and isolation in a three tank system," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1799-1813, August.

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