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

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

Content Provider	Scilit
Author	Gokul, T. Srinivasan, M. R.
Copyright Year	2021
Description	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.
Ending Page	536
Page Count	16
Starting Page	521
ISSN	20700237
e-ISSN	20700245
DOI	10.3329/jsr.v13i2.50479
Journal	Journal of Scientific Research
Issue Number	2
Volume Number	13
Language	English
Publisher	Bangladesh Academy of Sciences
Publisher Date	2021-05-01
Access Restriction	Open
Subject Keyword	Statistics and Probability Longitudinal Data Joint Modelling Missing Entries
Content Type	Text
Resource Type	Article

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