Document Type
Thesis
Degree
Master of Science (MS)
Major/Program
Statistics
First Advisor's Name
B.M. Golam Kibria
First Advisor's Committee Title
Committee chair
Second Advisor's Name
Florence George
Second Advisor's Committee Title
Co-Committee Chair
Third Advisor's Name
Sneh Gulati
Third Advisor's Committee Title
Committee member
Keywords
Signal to Noise, Signal, Noise, Signal to Noise Ratio, Ratio
Date of Defense
6-16-2023
Abstract
SNR is a measure of the strength of desired data relative to undesigned data. Population SNR is equal to the population mean divided by the population standard deviation. In practice, commonly in image processing, a high SNR means that the signal strength is stronger in relation to the noise. Having higher SNR provides more useful information. This thesis considers fifteen existing and proposed test statistics for testing the population SNR. A theoretical comparison among the test statistics is not possible, a Monte Carlo simulation study has been conducted. The performance of the test statistics is based on the empirical size and power of the tests considering a significance level 0.05. The simulation study resulted that some existing and proposed methods are performing well in some conditions. However, Method 10 performed the best in all simulation conditions. Three real life data are analyzed to illustrate the performance of the test statistics.
Identifier
FIDC011218
Recommended Citation
Menendez, Samantha, "Some Modified Test Statistics for Testing the Population Signal to Noise Ratio (SNR)" (2023). FIU Electronic Theses and Dissertations. 5347.
https://digitalcommons.fiu.edu/etd/5347
Included in
Applied Statistics Commons, Other Statistics and Probability Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons
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