Please use this identifier to cite or link to this item:

Title: A dimension reduction technique for estimation in linear mixed models
Authors: Carvalho, Miguel
Fonseca, Miguel
Oliveira, Manuela
Mexia, João
Editors: Krutchkoff, Richard G.
Ahmed, Ejaz S.
Ahn, Sung K.
Bretz, Frank
Chen, Din
Chenouri, Shojaeddin
Cheng, Guang
Dasgupta, Nairanjana
Fonnesbeck, Christopher J.
Habing, Brian
Hwang, Sun Young
Jiang, Wei
Jona-Lassinio, Giovanna
Kulasekera, K. B.
Lawrence, Kenneth D.
Lio, Y. L.
Martin, Michael A.
McKean, Joseph W.
Molchanov, Ilya
Neuhäuser, Markus
Ng, Angus S.K.
Ni, Liqiang
Onar, Arzu
Owen, William J.
Paul, Rajib
Peiris, Shelton
Shu, Linjie
Thomas, Fridtjof
Volodin, Andrei
Xiang, Liming
Xu, Xinyi
Ye, Keying
Zhang, Ying
Shanmugam, Ram
Bowman, K. O.
Johnson, Mark E.
Martz, Harry F.
Scott, E. Marian
Keywords: maximum-likelihood estimation; linear mixed models; stochastic optimization
Issue Date: 12-Sep-2011
Publisher: Journal of Statistical Computation and Simulation. Taylor & Francis
Abstract: This paper proposes a dimension reduction technique for estimation in linear mixed models. Specifically,we show that in a linear mixed model, the maximum-likelihood (ML) problem can be rewritten as a substantially simpler optimization problem which presents at least two main advantages: the number of variables in the simplified problem is lower and the search domain of the simplified problem is a compact set. Whereas the former advantage reduces the computational burden, the latter permits the use of stochastic optimization methods well qualified for closed bounded domains. The developed dimension reduction technique makes the computation of ML estimates, for fixed effects and variance components, feasible with large computational savings. Computational experience is reported here with the results evidencingan overall good performance of the proposed technique.
Type: article
Appears in Collections:CIMA - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

Files in This Item:

File Description SizeFormat
Journal of Statistical Computation and Simulation 2011 de Carvalho.pdf112.02 kBAdobe PDFView/OpenRestrict Access. You can Request a copy!
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

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


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois