Statistical and Computational Aspects of Mixed Model Analysis

Arthur P. Dempster, Chandu M. Patel, Murray R. Selwyn and Arthur J. Roth

Journal of the Royal Statistical Society Series C, 1984, vol. 33, issue 2, 203-214

Abstract: Statistical and computational techniques for the analysis of data from a normal mixed model with two variances are discussed and illustrated. Two iterative algorithms for restricted maximum likelihood estimation (REML) of the variances are compared. It is shown that these algorithms are much simplified by the use of a preliminary eigenvalue–eigenvector analysis. Two numerical examples are used to illustrate the theory by showing how variance estimates are used in the estimation and testing of fixed effects in the model. Monte Carlo simulations indicate that actual alpha levels of the tests are close to the nominal levels despite the estimation of the variance components. Diagnostic techniques are employed to assess model assumptions.

Date: 1984
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2307/2347446

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:bla:jorssc:v:33:y:1984:i:2:p:203-214

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().