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Singer, J.D. and Willett, J.B. (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press, New York.
https://doi.org/10.1093/acprof:oso/9780195152968.001.0001

has been cited by the following article:

• TITLE: High Order Tensor Forms of Growth Curve Models

  AUTHORS: Zerong Lin, Dongzhe Liu, Xueying Liu, Lingling He, Changqing Xu

  KEYWORDS: Tensor, Generalized Linear Model, Growth Curve Model, Parameter Estimation, Generalized Inverse

  JOURNAL NAME: Advances in Linear Algebra & Matrix Theory, Vol.8 No.1, March 1, 2018

  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.