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|
|Editors: ||Krutchkoff, Richard G.|
Ahmed, Ejaz S.
Ahn, Sung K.
Fonnesbeck, Christopher J.
Hwang, Sun Young
Kulasekera, K. B.
Lawrence, Kenneth D.
Lio, Y. L.
Martin, Michael A.
McKean, Joseph W.
Ng, Angus S.K.
Owen, William J.
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.|
|Appears in Collections:||CIMA - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.