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Journal volumes: 17
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:: Volume 17, Issue 1 (8-2020) ::
JSRI 2020, 17(1): 19-44 Back to browse issues page
Semi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses
Omid Khazaei1 , Mojtaba Ganjali 2, Mojtaba Khazaei1
1- Shahid Beheshti University
2- Shahid Beheshti University , m-ganjali@sbu.ac.ir
Abstract:   (501 Views)
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.
 
Keywords: Semi-parametric Quantile regression, continuous longitudinal data, local polynomial kernel, asymmetric Laplace distribution, semi-parametric model, Gibbs sampling.
Full-Text [PDF 1167 kb]   (585 Downloads)    
Type of Study: Applicable | Subject: General
Received: 2021/03/1 | Accepted: 2022/05/30 | Published: 2020/08/22
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Khazaei O, Ganjali M, Khazaei M. Semi-parametric Quantile Regression for Analysing Continuous Longitudinal Responses. JSRI 2020; 17 (1) :19-44
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Volume 17, Issue 1 (8-2020) Back to browse issues page
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