Mean driven balance and uniformly best linear unbiased estimators

Roman Zmyślony (), João Mexia, Francisco Carvalho () and Inês Sequeira

Statistical Papers, 2016, vol. 57, issue 1, 43-53

Abstract: The equivalence of ordinary least squares estimators (OLSE) and Gauss–Markov estimators for models with variance–covariance matrix $$\sigma ^2{\mathbf M}$$ σ 2 M is extended to derive a necessary and sufficient balance condition for mixed models with mean vector $${\varvec{\mu }}={{\mathbf X} {\varvec{\beta }}}$$ μ = X β , with $${\mathbf {X}}$$ X an incidence matrix, having OLSE for $$\varvec{\beta }$$ β that are best linear unbiased estimator whatever the variance components. This approach leads to least squares like estimators for variance components. To illustrate the range of applications for the balance condition, interesting special models are considered. Copyright Springer-Verlag Berlin Heidelberg 2016

Keywords: OLSE; Orthogonal block structure models; UBLUE; Kruskal condition (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00362-014-0638-y (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:spr:stpapr:v:57:y:2016:i:1:p:43-53

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-014-0638-y

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().