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

Xiao Min (), Chen Ting (), Huang Kunpeng () and Ming Ruixing ()
Additional contact information
Xiao Min: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou310018, China
Chen Ting: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou310018, China
Huang Kunpeng: Hunan University of Humanities, Science and Technology, Loudi417000, China
Ming Ruixing: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou310018, China

Journal of Systems Science and Information, 2020, vol. 8, issue 6, 549-564

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.

Keywords: high-dimensional diagonal covariance matrix; geometric shrinkage estimator; small sample; optimal shrinkage parameter; likelihood-unbiased estimator; hotelling’s T2 test (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.21078/JSSI-2020-549-16 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:8:y:2020:i:6:p:549-564:n:3

DOI: 10.21078/JSSI-2020-549-16

Access Statistics for this article

Journal of Systems Science and Information is currently edited by Shouyang Wang

More articles in Journal of Systems Science and Information from De Gruyter
Bibliographic data for series maintained by Peter Golla ().