Optimal shrinkage estimation of variances with applications to microarray data analysis

Tiejun Tong, Yuedong Wang

Research output: Contribution to journalJournal articlepeer-review

43 Citations (Scopus)


Microarray technology allows a scientist to study genomewide patterns of gene expression. Thousands of individual genes are measured with a relatively small number of replications, which poses challenges to traditional statistical methods. In particular, the gene-specific estimators of variances are not reliable and gene-by-gene tests have low powers. In this article we propose a family of shrinkage estimators for variances raised to a fixed power. We derive optimal shrinkage parameters under both Stein and squared loss functions. Our results show that the standard sample variance is inadmissible under either loss function. We propose several estimators for the optimal shrinkage parameters and investigate their asymptotic properties under two scenarios: large number of replications and large number of genes. We conduct simulations to evaluate the finite sample performance of the data-driven optimal shrinkage estimators and compare them with some existing methods. We construct F-like statistics using these shrinkage variance estimators and apply them to detect differentially expressed genes in a microarray experiment. We also conduct simulations to evaluate performance of these F-like statistics and compare them with some existing methods.

Original languageEnglish
Pages (from-to)113-122
Number of pages10
JournalJournal of the American Statistical Association
Issue number477
Publication statusPublished - Mar 2007
Externally publishedYes

User-Defined Keywords

  • F-like statistic
  • Gene expression data
  • Inadmissibility
  • James–Stein shrinkage estimator
  • Loss function


Dive into the research topics of 'Optimal shrinkage estimation of variances with applications to microarray data analysis'. Together they form a unique fingerprint.

Cite this