A sparse implementation of the average information algorithm for factor analytic and reduced rank variance models

A - Papers appearing in refereed journals

Thompson, R., Cullis, B. R., Smith, A. and Gilmour, A. 2003. A sparse implementation of the average information algorithm for factor analytic and reduced rank variance models. Australian & New Zealand Journal of Statistics. 45 (4), pp. 445-459.

AuthorsThompson, R., Cullis, B. R., Smith, A. and Gilmour, A.
Abstract

Factor analytic variance models have been widely considered for the analysis of multivariate data particularly in the psychometrics area. Recently Smith, Cullis & Thompson (2001) have considered their use in the analysis of multi-environment data arising from plant improvement programs. For these data, the size of the problem and the complexity of the variance models chosen to account for spatial heterogeneity within trials implies that standard algorithms for fitting factor analytic models can be computationally expensive. This paper presents a sparse implementation of the average information algorithm (Gilmour, Thompson & Cullis, 1995) for fitting factor analytic and reduced rank variance models.

KeywordsStatistics & Probability
Year of Publication2003
JournalAustralian & New Zealand Journal of Statistics
Journal citation45 (4), pp. 445-459
Digital Object Identifier (DOI)doi:10.1111/1467-842X.00297
Open accessPublished as non-open access
Funder project or code445
513
Research in statistics relevant to biological processes
Output statusPublished
Publication dates
Print17 Oct 2003
Publication process dates
Accepted01 Jan 2003
Copyright licensePublisher copyright
PublisherWiley
ISSN1369-1473

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