Robust estimation in joint modelling for human intelligence
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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.
keywordsGeneralized Linear Mixed Model, Joint Model, Influential Observations, Robust Estimation.Authors biographyDepartment of Statistics - University ofMadras - CHENNAIM - India (e-mai: gokultvasan@gmail.com).School of Mathematics and Statistics - University of Hyderabad - TELANGANA - India (e-mail: mrsrin8@gmail.com). Dipartimento di Scienze Umane e Sociali - Universita` di Napoli ’L’Orientale’ - NAPOLI - Italia (e-mail: mgallo@unior.it). |
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