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High Order Tensor Forms of Growth Curve Models
School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
School of Energy and Environment, City University of Hong Kong, Hong Kong, China
Zhejiang University of Water Resources and Electric Power, Hang Zhou, China
School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
- 1 School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
- 2 School of Energy and Environment, City University of Hong Kong, Hong Kong, China
- 3 Zhejiang University of Water Resources and Electric Power, Hang Zhou, China
- 4 School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
- 5 School of Mathematics and Physics, Suzhou University of Science and Technology, Suzhou, China
Advances in Linear Algebra & Matrix Theory·Volume 08 (2018)·Pages 18–32·Published 9 January 2018·DOI10.4236/alamt.2018.81003
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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.
KeywordsTensorGeneralized Linear ModelGrowth Curve ModelParameter EstimationGeneralized Inverse
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