Unbiased invariant minimum norm estimation in generalized growth curve model |
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Authors: | Xiaoyong Wu Guohua Zou Jianwei Chen |
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Affiliation: | a Department of Mathematics and Statistics, University of Windsor, Windsor, Ont., Canada N9B 3P4 b Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China c Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, NY 14642, USA |
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Abstract: | This paper considers the generalized growth curve model subject to R(Xm)⊆R(Xm-1)⊆?⊆R(X1), where Bi are the matrices of unknown regression coefficients, Xi,Zi and U are known covariate matrices, i=1,2,…,m, and E splits into a number of independently and identically distributed subvectors with mean zero and unknown covariance matrix Σ. An unbiased invariant minimum norm quadratic estimator (MINQE(U,I)) of tr(CΣ) is derived and the conditions for its optimality under the minimum variance criterion are investigated. The necessary and sufficient conditions for MINQE(U,I) of tr(CΣ) to be a uniformly minimum variance invariant quadratic unbiased estimator (UMVIQUE) are obtained. An unbiased invariant minimum norm quadratic plus linear estimator (MINQLE(U,I)) of is also given. To compare with the existing maximum likelihood estimator (MLE) of tr(CΣ), we conduct some simulation studies which show that our proposed estimator performs very well. |
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Keywords: | 62H12 62J05 |
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