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Activity Number: 219
Type: Topic Contributed
Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #312743
Title: On Estimation of Block Toeplitz Covariance Matrix in Mixed Linear Models
Author(s): Tatjana Von Rosen*+
Companies: Stockholm University
Keywords: Circular block symmetry ; Variance components ; Identifiability ; Maximum likelihood estimator ; Restricted model
Abstract:

Mixed linear models offer large flexibility in modelling data structures having some pattern, for example nestedness, circularity or symmetry. Structured data naturally arise in various applications including sociology, education, biology and signal processes. In this paper, we study mixed linear models with block Toeplitz covariance structures. We derive sufficient conditions for obtaining explicit and unique estimators for the variance-covariance components. It will be demonstrated that in order to obtain explicit maximum likelihood estimators, a restricted model must be considered, i.e. constraints on the covariance matrix must be imposed. Several different kinds of constraints exist that are sufficient to make the covariance parameters identifiable which, however, may not preserve the structure of the original covariance matrix. Moreover, not all of them are appropriate for real-life applications. We discuss different restricted models and obtain the maximum likelihood estimators for model parameters.


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