Robust Estimation in Partial Linear Mixed Model for Longitudinal Data

2008 ◽  
Vol 28 (2) ◽  
pp. 333-347 ◽  
Author(s):  
Qin Guoyou ◽  
Zhu Zhongyi
Keyword(s):  
Longitudinal Data ◽  
Robust Estimation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Partial Linear

scholarly journals Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models

Methodology ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 271-295
Author(s):  
Fabio Mason ◽  
Eva Cantoni ◽  
Paolo Ghisletta
Keyword(s):  
Longitudinal Data ◽  
Confidence Intervals ◽  
Robust Estimation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Parametric Bootstrap ◽  
Estimation Methods ◽  
Low Probability ◽  
Coverage Rates ◽  
Comprehensive Study

The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.

scholarly journals Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

2019 ◽  
Vol 35 (23) ◽  
pp. 4879-4885 ◽  
Author(s):  
Chao Ning ◽  
Dan Wang ◽  
Lei Zhou ◽  
Julong Wei ◽  
Yuanxin Liu ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Software Package ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

Joint Modeling for Longitudinal Data with Missing Values: A Bayesian Perspective on Human Intelligence

2021 ◽  
Vol 13 (2) ◽  
pp. 521-536
Author(s):  
T. Gokul ◽  
M. R. Srinivasan
Keyword(s):  
Longitudinal Data ◽  
Missing Values ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Joint Modeling ◽  
Model Framework ◽  
Response Data ◽  
Count Response ◽  
Longitudinal Count Data

Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

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