en عمومى General كاربردي Applicable فارسی <span style="font-size:11pt"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><span new="" roman="" style="font-family:" times="">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&#39;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&#39;s application, a Peabody Individual Achievement Test (PIAT) dataset is analyzed.</span></span></span></span></span><br> &nbsp; Semi-parametric Quantile regression, continuous longitudinal data, local polynomial kernel, asymmetric Laplace distribution, semi-parametric model, Gibbs sampling. 19 44 http://jsri.srtc.ac.ir/browse.php?a_code=A-10-252-2&slc_lang=en&sid=1 Omid Khazaei امید خزایی o-khazaei@sbu.ac.ir 10031947532846002386 10031947532846002386 No Shahid Beheshti University Mojtaba Ganjali مجتبی گنجعلی m-ganjali@sbu.ac.ir 10031947532846002387 10031947532846002387 Yes Shahid Beheshti University Mojtaba Khazaei مجتبی خزایی m-khazaei@sbu.ac.ir 10031947532846002388 10031947532846002388 No Shahid Beheshti University