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An improved and explicit surrogate variable analysis procedure by coefficient adjustment

Author

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  • Seunggeun Lee
  • Wei Sun
  • Fred A. Wright
  • Fei Zou

Abstract

SummaryUnobserved environmental, demographic and technical factors canadversely affect the estimation and testing of the effects ofprimary variables. Surrogate variable analysis, proposed to tacklethis problem, has been widely used in genomic studies. To estimatehidden factors that are correlated with the primary variables,surrogate variable analysis performs principal component analysiseither on a subset of features or on all features, but weightingeach differently. However, existing approaches may fail to identifyhidden factors that are strongly correlated with the primaryvariables, and the extra step of feature selection and weightcalculation makes the theoretical investigation of surrogatevariable analysis challenging. In this paper, we propose an improvedsurrogate variable analysis, using all measured features, that has anatural connection with restricted least squares, which allows us tostudy its theoretical properties. Simulation studies and real-dataanalysis show that the method is competitive with state-of-the-artmethods.

Suggested Citation

  • Seunggeun Lee & Wei Sun & Fred A. Wright & Fei Zou, 2017. "An improved and explicit surrogate variable analysis procedure by coefficient adjustment," Biometrika, Biometrika Trust, vol. 104(2), pages 303-316.
    • Handle: RePEc:oup:biomet:v:104:y:2017:i:2:p:303-316.
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    File URL: http://hdl.handle.net/10.1093/biomet/asx018
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    Cited by:

    1. Jiahuan Guo & Huili Feng & Jiejie Sun & Penghe Cao & Weifeng Wang & Hong Chen & Yuanchun Yu, 2019. "Application of Cloud Model to Evaluation of Forest Soil Fertility: A Case in Chinese Fir Plantations in Southern China," Sustainability, MDPI, vol. 11(22), pages 1-13, November.
    2. Zachary R. McCaw & Sheila M. Gaynor & Ryan Sun & Xihong Lin, 2023. "Leveraging a surrogate outcome to improve inference on a partially missing target outcome," Biometrics, The International Biometric Society, vol. 79(2), pages 1472-1484, June.

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