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Optimal Estimation for Power of Variance with Application to Gene-Set Testing

Author

Listed:
  • Xiao Min
  • Chen Ting
  • Ming Ruixing

    (School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou310018, China)

  • Huang Kunpeng

    (Hunan University of Humanities, Science and Technology, Loudi417000, China)

Abstract

Detecting differential expression of genes in genom research (e.g., 2019-nCoV) is not uncommon, due to the cost only small sample is employed to estimate a large number of variances (or their inverse) of variables simultaneously. However, the commonly used approaches perform unreliable. Borrowing information across different variables or priori information of variables, shrinkage estimation approaches are proposed and some optimal shrinkage estimators are obtained in the sense of asymptotic. In this paper, we focus on the setting of small sample and a likelihood-unbiased estimator for power of variances is given under the assumption that the variances are chi-squared distribution. Simulation reports show that the likelihood-unbiased estimators for variances and their inverse perform very well. In addition, application comparison and real data analysis indicate that the proposed estimator also works well.

Suggested Citation

  • Xiao Min & Chen Ting & Ming Ruixing & Huang Kunpeng, 2020. "Optimal Estimation for Power of Variance with Application to Gene-Set Testing," Journal of Systems Science and Information, De Gruyter, vol. 8(6), pages 549-564, December.
    • Handle: RePEc:bpj:jossai:v:8:y:2020:i:6:p:549-564:n:3
    DOI: 10.21078/JSSI-2020-549-16
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    References listed on IDEAS

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