scholarly journals A latent factor linear mixed model for high-dimensional longitudinal data analysis

2013 ◽  
Vol 32 (24) ◽  
pp. 4229-4239 ◽  
Author(s):  
Xinming An ◽  
Qing Yang ◽  
Peter M. Bentler
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Longitudinal Data Analysis ◽  
High Dimensional ◽  
Latent Factor

scholarly journals Real longitudinal data analysis for real people: Building a good enough mixed model

2009 ◽  
Vol 29 (4) ◽  
pp. 504-520 ◽  
Author(s):  
Jing Cheng ◽  
Lloyd J. Edwards ◽  
Mildred M. Maldonado-Molina ◽  
Kelli A. Komro ◽  
Keith E. Muller
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Mixed Model ◽  
Longitudinal Data Analysis ◽  
Real People

scholarly journals Improved mixed model for longitudinal data analysis using shrinkage method

2018 ◽  
Vol 12 (4) ◽  
pp. 305-312
Author(s):  
M. Rahmani ◽  
M. Arashi ◽  
N. Mamode Khan ◽  
Y. Sunecher
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Mixed Model ◽  
Longitudinal Data Analysis ◽  
Shrinkage Method

scholarly journals Estimating the Variance of Estimator of the Latent Factor Linear Mixed Model Using Supplemented Expectation-Maximization Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1286
Author(s):  
Yenni Angraini ◽  
Khairil Anwar Notodiputro ◽  
Henk Folmer ◽  
Asep Saefuddin ◽  
Toni Toharudin
Keyword(s):  
Longitudinal Data ◽  
Expectation Maximization ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Asymptotic Variance ◽  
Latent Factor ◽  
Second Moment ◽  
Variance Matrix ◽  
Testing Procedures ◽  
Sem Algorithm

This paper deals with symmetrical data that can be modelled based on Gaussian distribution, such as linear mixed models for longitudinal data. The latent factor linear mixed model (LFLMM) is a method generally used for analysing changes in high-dimensional longitudinal data. It is usual that the model estimates are based on the expectation-maximization (EM) algorithm, but unfortunately, the algorithm does not produce the standard errors of the regression coefficients, which then hampers testing procedures. To fill in the gap, the Supplemented EM (SEM) algorithm for the case of fixed variables is proposed in this paper. The computational aspects of the SEM algorithm have been investigated by means of simulation. We also calculate the variance matrix of beta using the second moment as a benchmark to compare with the asymptotic variance matrix of beta of SEM. Both the second moment and SEM produce symmetrical results, the variance estimates of beta are getting smaller when number of subjects in the simulation increases. In addition, the practical usefulness of this work was illustrated using real data on political attitudes and behaviour in Flanders-Belgium.

scholarly journals Ultra high‐dimensional semiparametric longitudinal data analysis

Biometrics ◽  
2020 ◽  
Author(s):  
Brittany Green ◽  
Heng Lian ◽  
Yan Yu ◽  
Tianhai Zu
Keyword(s):  
Data Analysis ◽  
Longitudinal Data ◽  
Longitudinal Data Analysis ◽  
High Dimensional
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