Confidence Intervals for Common Signal-to-Noise Ratio of Several Log-Normal Distributions
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Log-normal distribution is used widely in application fields such as economics and finance. This paper considers confidence interval estimates for common signal-to-noise ratio of log-normal distributions based on generalized confidence interval (GCI), adjusted method of variance estimates recovery, and computational approaches. A simulation study is conducted to compare the performance of these confidence intervals. A Monte Carlo simulation is applied to report coverage probability and average length of the confidence intervals. Based on the simulation study, for \(k=\) 3, the GCI can be used. For \(k=\) 6, the results of GCI approach perform similarly to the results of computational approach. For \(k=\) 10, the computational approach can be considered as an alternative to estimate the confidence interval. A numerical example based on real data is presented to illustrate the proposed approaches.
KeywordsAverage length Coverage probability Log-normal distribution Monte Carlo simulation Signal-to-noise ratio
This research was funded by King Mongkut’s University of Technology North Bangkok. Grant No. KMUTNB-61-GOV-D-30.
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Conflict of interest
The authors declare that they have no conflict of interest.
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