Distribution-free estimators of variance components for multivariate linear mixed models
Jun Han
Journal of Nonparametric Statistics, 2011, vol. 23, issue 1, 219-235
Abstract:
Non-iterative, distribution-free, and unbiased estimators of variance components including minimum norm quadratic unbiased estimators and the method of moments estimators are derived for multivariate linear mixed models. A general inter-cluster variance matrix, a same-member only general inter-response variance matrix, and an uncorrelated intra-cluster error structure for each response are assumed. Some properties of the proposed estimators such as unbiasedness and existence are discussed, and related computational issues are addressed. A simulation study is conducted to compare the proposed estimators with Gaussian (restricted) maximum likelihood estimators in terms of bias and mean square error. An application of gene expression family study is presented to illustrate the proposed estimators.
Date: 2011
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2010.507868 (text/html)
Access to full text is restricted to subscribers.
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:taf:gnstxx:v:23:y:2011:i:1:p:219-235
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20
DOI: 10.1080/10485252.2010.507868
Access Statistics for this article
Journal of Nonparametric Statistics is currently edited by Jun Shao
More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().