Please use this identifier to cite or link to this item: https://une.intersearch.com.au/unejspui/handle/1959.11/3075
Title: Parameter expansion for estimation of reduced rank covariance matrices
Contributor(s): Meyer, Karin (author)
Publication Date: 2008
DOI: 10.1186/1297-9686-40-1-3
Handle Link: https://hdl.handle.net/1959.11/3075
Abstract: Parameter expanded and standard expectation maximisation algorithms are described for reduced rank estimation of covariance matrices by restricted maximum likelihood, fitting the leading principal components only. Convergence behaviour of these algorithms is examined for several examples and contrasted to that of the average information algorithm, and implications for practical analyses are discussed. It is shown that expectation maximisation type algorithms are readily adapted to reduced rank estimation and converge reliably. However, as is well known for the full rank case, the convergence is linear and thus slow. Hence, these algorithms are most useful in combination with the quadratically convergent average information algorithm, in particular in the initial stages of an iterative solution scheme.
Publication Type: Journal Article
Source of Publication: Genetics Selection Evolution, 40(1), p. 3-24
Publisher: BioMed Central Ltd
Place of Publication: London, UK
ISSN: 0999-193X
Field of Research (FOR): 060412 Quantitative Genetics (incl Disease and Trait Mapping Genetics)
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Statistics to Oct 2018: Visitors: 90
Views: 90
Downloads: 0
Appears in Collections:Journal Article

Files in This Item:
2 files
File Description SizeFormat 
Show full item record

Page view(s)

82
checked on Feb 8, 2019
Google Media

Google ScholarTM

Check

Altmetric

SCOPUSTM   
Citations

 

Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.