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netQDA: Local Network-Guided High-Dimensional Quadratic Discriminant Analysis

Author

Listed:
  • Xueping Zhou

    (Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15216, USA)

  • Wei Chen

    (Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA)

  • Yanming Li

    (Department of Biostatistics & Data Science, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, KS 66160, USA)

Abstract

Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Suggested Citation

  • Xueping Zhou & Wei Chen & Yanming Li, 2024. "netQDA: Local Network-Guided High-Dimensional Quadratic Discriminant Analysis," Mathematics, MDPI, vol. 12(23), pages 1-21, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3823-:d:1535590
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    References listed on IDEAS

    as
    1. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    2. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    3. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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