Variable selection in linear mixed models using an extended class of penalties
Date
2012
Authors
Taylor, J.
Verbyla, A.
Cavanagh, C.
Newberry, M.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Australian and New Zealand Journal of Statistics, 2012; 54(4):427-449
Statement of Responsibility
Julian D. Taylor, Arūnas P. Verbyla, Colin Cavanagh and Marcus Newberry
Conference Name
Abstract
There is an emerging need to advance linear mixed model technology to include variable selection methods that can simultaneously choose and estimate important effects from a potentially large number of covariates. However, the complex nature of variable selection has made it difficult for it to be incorporated into mixed models. In this paper we extend the well known class of inline image penalties and show that they can be integrated succinctly into a linear mixed model setting. Under mild conditions, the estimator obtained from this mixed model penalised likelihood is shown to be consistent and asymptotically normally distributed. A simulation study reveals that the extended family of penalties achieves varying degrees of estimator shrinkage depending on the value of one of its parameters. The simulation study also shows there is a link between the number of false positives detected and the number of true coefficients when using the same penalty. This new mixed model variable selection (MMVS) technology was applied to a complex wheat quality data set to determine significant quantitative trait loci (QTL).
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2012 Australian Statistical Publishing Association Inc.