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Separating Signals and Noises Using Mixture Model and Multiple Testing
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 Title & Authors
Separating Signals and Noises Using Mixture Model and Multiple Testing
Park, Hae-Sang; Yoo, Si-Won; Jun, Chi-Hyuck;
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A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.
Signal;noise;EM algorithm;false discovery rate;mixture model;multiple testing;
 Cited by
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