Covariance based moment equations for improved variance component estimation

Sanjay Chaudhuri, Tatsuya Kubokawa, Shonosuke Sugasawa

Research output: Contribution to journalArticlepeer-review


ANOVA-based estimators of variance components for nested-error regression models are always constructed based on moment equations through residual variance. We consider moment equations associated with residual covariance and construct improved ANOVA-based estimators. The proposed estimators have closed-form analytic expressions, which enables easy computation. Moreover, they are shown to be consistent, asymptotically unbiased, and robust to the choice of distribution of the random effects. These estimators have comparable and often better performances than many traditional estimators of variance components like the Prasad-Rao, maximum likelihood, and the restricted maximum likelihood estimators for almost all kinds of sample allocations. Their improved performances are demonstrated analytically as well as through detailed simulation studies and applications to real data sets.

Original languageEnglish
Pages (from-to)1290-1318
Number of pages29
Issue number6
Publication statusPublished - 2022
Externally publishedYes


  • cross-products
  • estimating equations
  • Nested error regression model
  • unbiased estimation
  • variance components

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Covariance based moment equations for improved variance component estimation'. Together they form a unique fingerprint.

Cite this