Separating Signals and Noises Using Mixture Model and Multiple Testing

혼합모델 및 다중 가설 검정을 이용한 신호와 잡음의 분류

  • Park, Hae-Sang (Department of Industrial and Management Engineering POSTECH) ;
  • Yoo, Si-Won (Customer Satisfaction Improvement Team, NHN) ;
  • Jun, Chi-Hyuck (Department of Industrial and Management Engineering, POSTECH)
  • 박해상 (포항공과대학교 산업경영공학과) ;
  • 유시원 (NHN 고객만족추진팀) ;
  • 전치혁 (포항공과대학교 산업경영공학과)
  • Published : 2009.08.31


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.


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