Abstract
Sharma and Krishna [16] derived mathematically an appealing asymptotic confidence interval for the population Signal-to-Noise Ratio (SNR). In this paper, an evaluation of the performance of this interval using Monte Carlo simulations using randomly generated data from normal, log-normal, χ2, Gamma, and Weibull distributions three of which are discussed in Sharma and Krishna [16]. Simulations revealed that its performance, as measured by coverage probability, is totally dependent on the amount of noise introduced. A proposal for using ranked set sampling (RSS) instead of simple random sampling (SRS) improved its performance. It is recommended against using this confidence interval for data from a log-normal distribution. Moreover, this interval performs poorly in all other distributions unless the SNR is around one.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Statistics and Probability
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
