A block-diagonal growth curve model

Authors:
Luzhou Xu;Petre Stoica;Jian Li
Affiliations:
Department of Electrical and Computer Engineering, P.O. Box 116130, University of Florida, Gainesville, USA;Department of Information Technology, Uppsala University, Uppsala, Sweden;Department of Electrical and Computer Engineering, P.O. Box 116130, University of Florida, Gainesville, USA
Venue:
Digital Signal Processing
Year:
2006

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Abstract

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.