Robust estimation in joint modelling for human intelligence - Michele Gallo, Thanigaivasan Gokul, Mamandur Rangaswamy Srinivasan - Vita e Pensiero - Articolo Statistica & Applicazioni

Robust estimation in joint modelling for human intelligence

newdigital Robust estimation in joint modelling for human intelligence
title Robust estimation in joint modelling for human intelligence

publisher Vita e Pensiero
format Article | Pdf
online since 07-2021
doi 10.26350/999999_000035
issn 1824-6672 (print) | 2283-6659 (digital)
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Joint models under generalized linear mixed model framework have received lot of attention among researchers in the field of psychology to analyse data with more than one response variable. The presence of aberrant observations in the data may influence the estimation of parameters in the existing method of estimation such as maximum likelihood, quasi-likelihood, etc. Hence, there exists a need for robust method of estimation under joint modelling to reduce the effect of influential data points. In this paper, two methods of robust estimation namely robust Maximum Likelihood method and robust Monte Carlo Newton-Raphson for joint longitudinal model has been compared with the usual maximum likelihood method to examine the association between the outcome variables of Spearman’s G and S factors of human intelligence along with other covariates based on school lunch intervention data. In addition, a parametric bootstrap study is adopted to find the sensitivity and efficiency of the robust method in resampling techniques with varying sample sizes.


Generalized Linear Mixed Model, Joint Model, Influential Observations, Robust Estimation.

Authors biography

Department of Statistics - University ofMadras - CHENNAIM - India (e-mai:
School of Mathematics and Statistics - University of Hyderabad - TELANGANA - India (e-mail:
Dipartimento di Scienze Umane e Sociali - Universita` di Napoli ’L’Orientale’ - NAPOLI - Italia (e-mail:

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