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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/25871

Title: Fast, Closed-form, and Efficient Estimators for Hierarchical Models with AR(1) Covariance and Unequal Cluster Sizes
Authors: Hermans, Lisa
Nassiri, Vahid
Molenberghs, Geert
Kenward, Michael G.
Van der Elst, Wim
Aerts, Marc
Verbeke, Geert
Issue Date: 2017
Citation: Communications in statistics. Simulation and computation, 47 (5),p. 1492-1505
Status: In Press
Abstract: This article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time.
Notes: Hermans, L (reprint author), Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. lisa.hermans@uhasselt.be
URI: http://hdl.handle.net/1942/25871
DOI: 10.1080/03610918.2017.1316395
ISI #: 000434682800017
ISSN: 0361-0918
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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