4025

Omid Khazaei, Mojtaba Ganjali and Mojtaba Khazaei

مجله پژوهشهاي آماري ايران

Statistical Research and Training Center پژوهشكده آمار

2020

17

1

19-44

Semi-parametric Quantile regression , continuous longitudinal data , local polynomial kernel , asymmetric Laplace distribution , semiparametric model , Gibbs sampling

Recently, quantile regression (QR) models are often applied for longitudinal data analysis. When the distribution of responses seems to be skew and asymmetric due to outliers and heavy-tails, QR models may work suitably. In this paper, a semi-parametric quantile regression model is developed for analysing continuous longitudinal responses. The error term’s distribution is assumed to be Asymmetric Laplace (AL) distribution for modeling the continuous responses. The correlation of longitudinal responses belong to the same individual is taken into account by using a random-effects approach. We use the local polynomial kernel to approximate the non-parametric part of the model. The parameter estimation procedure is performed under a Bayesian paradigm using the Gibbs sampling method. The performance of the model is eva‎luated in a simulation study. To show the proposed model’s application, a Peabody Individual Achievement Test (PIAT) dataset is analyzed.