ALAMT  Vol.8 No.1 , March 2018
High Order Tensor Forms of Growth Curve Models
Abstract: In this paper, we first study the linear regression model and obtain a norm-minimized estimator of the parameter vector by using the g-inverse and the singular value decomposition of matrix X. We then investigate the growth curve model (GCM) and extend the GCM to a generalized GCM (GGCM) by using high order tensors. The parameter estimations in GGCMs are also achieved in this way.
Cite this paper: Lin, Z. , Liu, D. , Liu, X. , He, L. and Xu, C. (2018) High Order Tensor Forms of Growth Curve Models. Advances in Linear Algebra & Matrix Theory, 8, 18-32. doi: 10.4236/alamt.2018.81003.

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