A Nonstandard Method for Estimating a Linear G rowth Model in LISREL

1998 ◽  
Vol 22 (3) ◽  
pp. 453-473 ◽  
Author(s):  
Michael J. Rovine ◽  
Peter C.M. Molenaar

A structural equation modelling approach for estimating a linear growth curve model is presented. This method can be used for a wide variety of models based on the General Linear Mixed Model. This model is a simple example of a multilevel or random coefficients model. The logic of the method is described, and then a LISR E L implementation is presented. A s an example of the individual linear growth curve model, growth data presented by Pothoff and Roy (1964) is analysed.

Metrika ◽  
2011 ◽  
Vol 75 (8) ◽  
pp. 1069-1092 ◽  
Author(s):  
Katarzyna Filipiak ◽  
Dietrich von Rosen

1994 ◽  
Vol 19 (3) ◽  
pp. 201-215 ◽  
Author(s):  
Frantisek Mandys ◽  
Conor V. Dolan ◽  
Peter C. M. Molenaar

This article has two objectives. The first is to investigate in greater detail the finding of Rogosa and Willett that the quasi-Markov simplex model fits a linear growth curve covariance structure. It is found that under various circumstances the quasi-Markov simplex model is rejected. Furthermore, the procedure is reversed by fitting the linear growth curve to quasi-Markov simplex covariance structure. It is found that the linear growth curve, like the quasi-Markov simplex, is not always rejected even though the model is formally incorrect. The second objective of this article is to present a quasi-Markov simplex model with structured means. This model, like the linear growth curve model with structured means, is based on the assumption that the variation in means and individual differences are attributable to the same causal agents. We argue that this assumption should be tested explicitly. An example is given.


2005 ◽  
Vol 24 (8) ◽  
pp. 1139-1151 ◽  
Author(s):  
V. Chandrasekaran ◽  
G. Gopal ◽  
A. Thomas

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Xiaoxiao Song ◽  
Tao Tao ◽  
Tao Tao ◽  
Qi Zhao ◽  
Lars Palm ◽  
...  

To early detection of influenza outbreak in the rural China, we collected the 1-year data of ILI through the web-based syndromic surveillance system in rural China (ISSC). Linear growth curve model (LGM) can be used to predict growth trajectory of ILI over 7 days (one week) in each healthcare unit by the introduction of random effects. LGM is applicable in modeling the growth and variation of daily outpatient visits of ILI in rural healthcare units. The growth rate curves of ILI surveillance data might be useful for the early detection of influenza epidemic in rural China.


Sankhya A ◽  
2020 ◽  
Author(s):  
Shinpei Imori ◽  
Dietrich von Rosen ◽  
Ryoya Oda

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