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A block-diagonal growth curve model
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2006 (English)In: Digital signal processing (Print), ISSN 1051-2004, E-ISSN 1095-4333, Vol. 16, no 6, 902-912 p.Article in journal (Refereed) Published
Abstract [en]

We consider a variation of the growth curve (GC) model, referred to as the block-diagonal growth curve (BDGC) model, where the unknown regression coefficient matrix is constrained to be block-diagonal. A closed-form approximate maximum likelihood (AML) estimator for this model is derived based on the maximum likelihood principle. We analyze the statistical properties of this method theoretically and show that the AML estimate is unbiased and asymptotically statistically efficient for a large snapshot number. Via numerical examples in wireless communications, we also show that the proposed AML estimator can achieve excellent estimation accuracy.

Place, publisher, year, edition, pages
2006. Vol. 16, no 6, 902-912 p.
Keyword [en]
Block-diagonal vectorization, Cramér-Rao bound, Generalized Khatri-Rao product, Growth curve model, Maximum likelihood estimation
National Category
Information Science
URN: urn:nbn:se:uu:diva-26541DOI: 10.1016/j.dsp.2006.05.005ISI: 000243346900021OAI: oai:DiVA.org:uu-26541DiVA: diva2:54315
Available from: 2007-02-20 Created: 2007-02-20 Last updated: 2011-02-17Bibliographically approved

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Stoica, Peter
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