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

Estimating the Variance of Estimator of the Latent Factor Linear Mixed Model Using Supplemented Expectation-Maximization 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 mod...

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Bibliographic Details
Main Authors: Yenni Angraini, Khairil Anwar Notodiputro, Henk Folmer, Asep Saefuddin, Toni Toharudin
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Symmetry
Subjects:
latent factor linear mixed model (LFLMM)
expectation-maximization (EM) algorithm
supplemented EM algorithm
longitudinal data analysis
Online Access:https://www.mdpi.com/2073-8994/13/7/1286

Internet

https://www.mdpi.com/2073-8994/13/7/1286

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