Communications for Statistical Applications and Methods

  • 제28권2호
  • /
  • Pages.135-150
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  • 2021
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Classification via principal differential analysis

  • Jang, Eunseong (Department of Applied Statistics, Chung-Ang University) ;
  • Lim, Yaeji (Department of Applied Statistics, Chung-Ang University)
  • 투고 : 2020.06.24
  • 심사 : 2021.01.04
  • 발행 : 2021.03.31
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초록

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

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참고문헌

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