Robust estimation in joint modelling for human intelligence

digital

Article

Magazine

STATISTICA & APPLICAZIONI

Section

Open Access

Title

Robust estimation in joint modelling for human intelligence

Authors

Michele Gallo, Thanigaivasan Gokul, Mamandur Rangaswamy Srinivasan

Publisher

Vita e Pensiero

Format

Article |

Pdf

Online from

07-2021

Doi

10.26350/999999_000035

Issn

1824-6672 (print) | 2283-6659 (digital)

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Description Readers' comments

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.

keywords

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

Authors biography

Department 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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