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

1,† and 2,*,†
1
Department of Statistics, Faculty of Science, Ramkhamhaeng University, Bangkok 10240, Thailand
2
Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Stats 2019, 2(1), 164-173; https://doi.org/10.3390/stats2010012
Received: 13 February 2019 / Revised: 25 February 2019 / Accepted: 26 February 2019 / Published: 27 February 2019
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PDF [758 KB, uploaded 27 February 2019]

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. View Full-Text
Keywords: signal-to-noise ratio; log-normal distribution; MOVER approach; GCI approach signal-to-noise ratio; log-normal distribution; MOVER approach; GCI approach
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Thangjai, W.; Niwitpong, S.-A. Confidence Intervals for the Signal-to-Noise Ratio and Difference of Signal-to-Noise Ratios of Log-Normal Distributions. Stats 2019, 2, 164-173.

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